 2023 December 12
 2023 November 28
 2023 November 7
 2023 October 24
 2023 October 17
 2023 September 26
 August 29, 2023
 2023 July 25
 2023 April 18
 2023 March 28
 2023 February 14
 2023 January 31
 2022 November 1
 2022 October 18
 2022 October 11
 2022 23 August
 2022 June 14
 2021 September 21
 2018 January 23 (Attending statisticians: Chang Yu, Hakmook Kang, Li Wang)
 21 February, 2017
 14 February, 2017
 31 January, 2017
 20 December, 2016
 22 November, 2016
 25 October, 2016
 11 October, 2016
 27 September, 2016
 13 September, 2016
 23 August, 2016
 16 August, 2016
 2 August, 2016
 21 June, 2016
 10 May, 2016
 26 April, 2016
 12 April, 2016
 28 March, 2016
 02 February, 2016
 19 January, 2016
 12 January, 2016
 15 December, 2015
 08 December, 2015
 15 September, 2015
 30 June 15
 5 May 15
 14 April 15
 7 April 15
 17 March 15
 10 March 15
 20 January 15
 13 January 15
 6 January 15
 16 December 14
 11 November 14
 30 September 14
 26 August 14
 22 July 14
 13 May 14
 25 Feb 14
 5 Nov 13
 20 Aug 13
 13 Aug 13
 25 June 13
 14 May 13
 7 May 13
 23 April 13 (probable)
 19 March 13
 4 Dec 12
 6 Nov 12
 30 Oct 2012
 9 Oct 2012
 2 Oct 2012
 25 Sep 2012
 18 Sep 2012
 28 Aug 2012
 14 Aug 2012
 24 July 2012
 10 July 2012
 3 July 2012
 24 April 12
 17 April 12
 21 Feb 12
 14 Feb 12
 17Jan12
 20Dec11
 25 Oct 11
 2 Aug 11
 26 July 11
 5 July 11
 28 June 11
 24 May 11
 3 May 11
 8 Feb 11
 4 Jan 11
 14 Dec 10
 23 Nov 10
 2Nov10
 26Oct10
 5Oct10
 14Sep10
 31Aug10
 17Aug10
 22Jun10
 25May10
 18May10
 2Mar10
 15Dec09
 24Nov09 1Dec09 15Dec09
 17Nov09
 1Sep09
 25Aug09
 18Aug09
 21Jul09
 30Jun09
 7Apr09
 18Dec07
 13 May 2008
 17 Nov 2008
 20 Jan 2009
 24 Feb 2009
 3 Mar 2009
 10 Mar 2009
Omics Data Clinic Notes and Analyses
2023 December 12
Nicole Malofsky, Biomedical Engineering
Determining TB bacteria drug susceptibility is essential to match TBinfected patients with their most effective drug regimen. In this work, we are developing an approach for confirmation of drug susceptibility without requiring sequencing. Melt temperature (Tm) of PCR products offers a rapid technique for confirming susceptibility down to a single nucleotide polymorphism using basic PCR instruments. A drugresistant PCR product will produce a lower Tm than a drugsusceptible PCR product. In our application of isoniazid drug resistance, the mutations induce changes in Tm as small as 0.18ºC. These small changes in Tm are difficult to detect on current calibrated PCR instrumentation due to instrument heating variability across wells and potential salt concentration variability amongst samples.
Our reagentbased approach adds an additional reaction component to help detect these small Tm differences. This additive, LDNA, is designed to produce a reliable Tm representative of a drugsusceptible PCR product. In every sample, we can then compare this LDNA Tm to the PCR product Tm to identify if the PCR product was drugsusceptible or not. Notably, LDNA melting behavior is similar to that of a PCR product so these two Tm values will be proportionately affected by variance in heating. We are currently seeking to statistically assess this assumption.
To test this assumption, we used a 96well format. It is known that current instruments have inherent heating variability across the 96 wells. A recent experimental design placed 24 drugsusceptible samples in the top left quadrant of the 96well plate and 24 drugresistant samples in the top right quadrant of the 96well plate. The data was first analyzed by comparing Tm between PCR products of the two quadrants. An unpaired ttest of equal variance produced a pvalue of 0.55. We interpret this to mean the “goldstandard” method cannot identify the difference between our drugsusceptible and drugresistant samples. The data was next analyzed by subtracting LDNA Tm from PCR product Tm. These Tm difference values were compared between the two quadrants using an unpaired ttest of equal variance and produced a pvalue of 2.58E05. We interpret this to mean the LDNA method is indeed able to identify the difference between our drugsusceptible and drugresistant samples. Is our experimental design and statistical analysis appropriate to draw these conclusions?
Next, we seek to assess whether our LDNA additive can overcome variability in salt concentration amongst samples. This seems more complicated to test. All measurements affected by salt additives are also influenced by instrument heating variability. This adds a confounding variable to our testing.
To test this assumption, we used a 96well format. The experimental design placed 18 drugsusceptible samples in the top left quadrant of the 96well plate and 18 drugsusceptible samples with salt additive in the bottom left quadrant of the 96well plate. We first wanted to assess whether Tm would be affected by accidental salt additions during variable reaction prep, for example. The data was analyzed by comparing Tm between PCR products of the two quadrants. An unpaired ttest of equal variance produced a pvalue of 7.90E23. We interpret this to mean the addition of salt significantly changes our PCR product Tm. We then wanted to assess whether the Tm difference values were consistent with the addition of salt. The data was analyzed by subtracting LDNA Tm from PCR product Tm. These Tm difference values were compared between the two quadrants using an unpaired ttest of equal variance and produced a pvalue of 0.053. We interpret this to mean the Tm difference values are not different when salt concentration is increased in samples. This would indicate our LDNA comparison approach is not sensitive to changes in salt. Is our experimental design and statistical analysis appropriate to draw these conclusions?
2023 November 28
Jennifer Choe, Hematology/Oncology
I did a project with a pharma company that generated a raw data file of transcriptomic expression data in head and neck cancers. But I don’t know how to analyze the data myself –particularly in wanting to evaluate certain signaling pathways.
2023 November 7
Eric Wright, BME/PMI
I would like assistance in reviewing my RNAseq pipeline to identify problems.
2023 October 24
Uday Suresh, Department of Biomedical Information
Looking at audit log data and seeking help modeling behaviors based on aggregated observed measures.
2023 October 17
Eric Wright, BME/PMI
RNAseq on mouse liver; see good separation between groups by PC1, but very few genes with and adjusted pvalue below 0.05
2023 September 26
Carl Stone (Megan Behringer), Biological Sciences
I grew a barcoded E. coli library over a few days and sequenced each timepoint to determine barcode frequency. Each library was grown in the presence of three different strains and every combination of the three. I am trying to measure barcode fitness in the presence of different combinations of strains over time and use Bayesian inference to determine how strain partners influence barcode fitness. I have been working on a Bayesian GLMM to accomplish this (using R packages ALDEx2 to model barcode measurement error and logratio transform samples and brms for the GLMM). My goals for the session are 1) to determine if a GLMM is the best method for this question; 2) to check that my data normalization methods are appropriate; and 3) to construct a model that fits my data and experimental design.
August 29, 2023
Valeria M Reyes Ruiz and Juan Barraza, Pathology, Microbiology, and Immunology
We have engineered reporter constructs for each of the 16 Two component signaling systems in S. aureus. Through the application of advanced imaging modalities, we identified three S. aureus TCSs that are activated upon sensing hostimposed stresses inside macrophages. We then developed an arrayed genomewide CRISPR screen in macrophages with the use of robotics and highcontent imaging to discover host factors responsible for the control of the intracellular reservoir of S. aureus and for the activation of these regulatory systems. Our data analysis pipeline relies on the use of machine learning with robust segmentation to measure both TCSdependent reporter signal and constitutive bacterial signal on a percell basis for every well in our arrayed library. We are looking for help with the statistics on this dataset. We would like to identify hits and calculate confidence intervals for those hits. We have an idea of the data analysis, but would like to make sure everything is done appropriately with an expert on biostatistics.
2023 July 25
Mariah Caballero (Yolanda J. McDonald), Human and Organizational Development
Methodology review for data and spatiotemporal analysis strategy. Project entails community impacts (social and environmental dimensions) over nuclear energy development phases. Unit of analysis is census tracts within a 10mile radius of nuclear power plant location. We are looking at three distinct phases of operational development, including installation, operations & maintenance, and decommissioning.
2023 April 18
Julie Bejoy, Nephrology and Hypertension
M2 macrophage exosomes to treat complications of diabetic kidney disease.
2023 March 28
Julie Bejoy, Nephrology and Hypertension
We focus our VICTR project on using M2 macrophagederived exosomes on alleviating the complications associated with diabetes in kidney organoids. We will also do miRNA profiling of M2øExos by microRNA(mRNA) sequencing. The study will be performed on three or more samples on three or more independent occasions. We need guidance on statistical analysis plan for the grant application.
2023 February 14
Justin Jacobse (Yash Choksi), GIMedicine
The aim of this analysis is to analyze cell abundance. The four independent variables (patient groups) are categorical. The 57 dependent variables (each variable is a cell type) are continuous and arbitrary units. There are 94 independent measurements per cell type (these are the 94 patients in the four groups) and there is no missing data.
The question is how to best determine whether cell frequencies are different between the patient groups. Whether one cell frequency is different than another cell frequency is not relevant. I have done a PERMANOVA with posthoc SIMPER analysis and would like to verify this would be the correct analysis. The main reason to check is that it has been suggested that interpretation of SIMPER analysis is complex. Mentor confirmed.
2023 January 31
Nancy Newlin (Kurt Schilling), Computer Science
We want to explore how various pipeline parameters/decisions affect the reproducibility, repeatability, and bias of diffusion metrics (graph measures calculated from connectomes, bundle mean/length, FA/MD [all single scalar values]). We want to see specifically how changing one part of the pipeline (the number of streamlines/data points used during tractography) affects these things. We hope to define a certain number of streamlines/data points at which the pipeline becomes reproducible/repeatable/unbiased. We would like to know what tests we can use to determine reproducibly, repeatability and bias. Mentor confirmed.
2022 November 1
Carl Stone (Megan Behringer), Biological Sciences
We sequenced E. coli using nanopore sequencing to measure adenine methylation across the genome. This gives us the fraction of reads methylated or unmethylated across 38,000 sites in the genome, and we are comparing methylation at specific sites and genomewide between 11 samples (3 groups). We are trying to 1) identify which samples are significantly different and by how much and 2) identify which sites differ between samples. I've done PCA but the large number of dimensions, weird distribution of the data (median methylation percent of ~97% but highly skewed), and multicollinearity between sites makes it hard to interpret, so I am looking for guidance on PCA or other methods to answer my questions. Mentor confirmed.
2022 October 18
Monica Morales (Dolly Ann PadovaniClaudio), Vanderbilt Eye Institute
Data was collected to analyze the inflammatory response of cells (N=3) after stimulation with 4 molecules (IL1b, TNFa, IL8, IL6) compared with vehicle. QtPCR gene expression of the same molecules was made and fold change was analyzed by oneway ANOVA. Questions: 1. p values in some treatment groups are significant and in other are not; even when the fold change is 5 times grater. Why? 2. When different experiments are compared. Some ANOVAS have significant p values and other don't. Even when they have the same fold change distribution in between treatment groups. Why? 3. Is there other statistical analysis that would work better for our study? Mentor confirmed.
2022 October 11
Shannon Townsend (Maureen Gannon), Molecular Physiology & Biophysics
Project deals with examining gene expression by qRTPCR in cultured cells after different tretments singly and in combination.. Our issue is figuring out the best way to graph the data, as baseline measurements between treatments differs between groups. Mentor confirmed.
2022 23 August
Melissa Kimlinger (Josh Billings), Anesthesiology
My mentor and I have data from bulk RNAseq in mouse kidneys that were adminstered different oxygen treatments during a renal ischemia/reperfusion surgery. We have done some analyses on our own, including PCA analysis, heatmap/unsupervised clustering, and some pairwise comparisons to find differentially expressed genes and pathway analysis. As we are not statisticians, we would greatly appreciate expert input on next steps such as data presentation, statistical cutoffs, and recommended packages for pathway analysis. Mentor confirmed.
Justin Jacobse (Yash Choksi), GIMedicine
Eosinophilic esophagitis is a foodmediated disease characterized by tissue eosinophils. We have a dataset of pediatric and adult tissue RNAseq of esophagus. This data includes both patients and controls, and females and males. The dataset we use is composed of existing data as well as unpublished data from our lab. I use limma to analyze the RNAseq data and have a question regarding the definition of contrasts; specifically how to examine/specify contrasts if I want to assess the effect of gender on gene expression in disease (compared to no disease) separately for adults and peds in one model, i.e. without two separate models. Mentor confirmed.
2022 June 14
Ciara Shaver, Medicine/Pulmonary
I have LCMS/MS proteomic data from airspace fluid of organ donors. Samples are from about 15 patients and many have multiple timepoints. I need help analyzing the data into pathways most different between certain group and determining what proteins change most over time.
2021 September 21
Amanda MartinezLincoln, Alexandra Key, Special Education
Background: Proposal will examine shared reading in children and parents using hyperscanning. Specifically, the project will examine brain synchrony in parentchild dyads. Two groups: English Learners, Native English speakers. Two conditions: Englishonly text, bilingual text. We would like to address the justification of sample size and statistical analysis plan.
Meeting Notes: Received previous feedback to work on sample size and statistical analysis plan. The current budget only allows for 30 dyads (15 per year per group, 30 per group across two years). Aim 1: whether comprehension outcomes are moderated by language background (EL versus NE). Aim 2: EL only, whether comprehension outcomes are moderated by text type (English versus bilingual). Wanted to compare between conditions.
Recommendations:
 Find the mean and standard deviation for comprehension outcomes and brain synchrony (both Aim 1 and Aim 2) from similar studies. Could use multiple studies with similar comprehension outcomes and similar population. Share the information with Hakmook Kang so he can make a power analysis. No power analysis for Aim 3. In the SAP, mention that the study will be underpowered.
 To calculate circular correlation in MATLAB, first take the Hilbert transformation (`hilbert`). This function takes the data and outputs the transformed data. After getting the output, can do `angle` in MATLAB. Then do `circ_corrcc` (within Circular Statistics Toolbox). This function takes the (output from `angle` for one participant, output from `angle` for the other participant) and gives the correlation. Do this on each combination of the dyads. Yan can send over the code. Alexandra Key will provide the reference.
 Calculate the circular correlation separately for passive baseline, active baseline, and active conditions (two book reading conditions). Compare active condition separately with passive baseline, and with active baseline
2018 January 23 (Attending statisticians: Chang Yu, Hakmook Kang, Li Wang)
Joshua David Chew, Pediatric Cardiology Fellow
 The project is looking at pulmonary pulse transit time (pPTT), a novel marker for pediatric pulmonary arterial hypertension. There were N=21 PAH subjects with N=42 matched controls on age and sex.
 Two blinded reviewers completed the measurements on all of the studies. We reviewed the results of the ICC calculation and the BlandAltman plot.
 We suggested fit a Cox proportional hazard model for the outcome.
 $5000 VICTR voucher would be sufficient for the scope of the work.
Sarah Fuchs, Pediatric Cardiology Clinical Fellow
 Pediatric cardiology patients were classified into two groups, those with in utero findings of RAS at < 30 weeks gestation (group A, N=15) versus those with in utero findings of RAS at >= 30 weeks gestation (group B, N=5).
 The primary outcome is Postnatal Transplantfree Survival comparing between the two groups. Since the event rate was low (8 in group A and 1 in group B had transplant or death), we suggest report KaplanMeier curve and the logrank test for this pilot data and use it to plan for future study. Can also consider fit a Cox proportional hazard model using the actual gestational weeks of the RAS findings.
21 February, 2017
Joshua Beckman
I am a translational researcher who now has metabolomic data. This is a flux study wherein we have blood at baseline, after 5 minute ischemic stimulus and 1 minute after ischemic stimulus in healthy and diabetic subjects. I want to see if there is a difference at baseline, with the intervention, and how these baseline and flux changes associate with vascular function
14 February, 2017
Alissa Guarnaccia
I am a graduate student in Bill Tansey’s lab in Cell and Developmental biology.
I have three sets of proteomic datasets (each ~1500 data points) that I am trying to perform the appropriate statistics for. Each dataset individually has been analyzed statistically, but I need help doing statistics on an average of the three. I believe I need to perform a BenjaminiHochberg false discovery rate analysis to generate pvalues of each datapoint. Ultimately I’d like to generate a volcano plot.
High dimensional data is the theme my questions fall under.
31 January, 2017
Sophie Katz
I am a first year pediatric infectious diseases fellow and am in the very beginning stages of a project that would look at using a serum biomarker (procalcitonin) to aid antimicrobial stewardship efforts to pull off antibiotics earlier in the pediatric ICU settings. I'd love help with determining power and sample size, as well as any other advice you may be able to provide starting out.
20 December, 2016
Yuanjun Guo
I’m Yuanjun in Hind Lal’s lab in Pharmacology. I’d like to consult about one of our PCR array study design. I prefer to come at this Friday noon (12/16) if there is still place available. The general idea of our study is we found the phenotype markers are significantly changed when we overexpress the gene X we interest under both control and drugtreated condition. And we plan to move forward to find the target genes related with our gene and drug manipulation.
The
experimental groups are harvest from cells as listed
Treatment 
control 
control 
Treated 
Treated 
Cells 
WT cells 
Overexpression 
WT cells 
Overexpression 
The
goal of this study is to find target genes(those have significantly changed due to either overexpression of the protein VS WT in control and treated groups)
The
method we plan to run qRTPCR using a 384well predesign PCR plates (96*4, for each 96well part, there are
91 different target primers)
My main problems are what the minimal or proper replications I need for this study and if the sample size here will affect the
cutoff later to pick up the genes significantly changed due to manipulation.
So far my plan is to have 34 different biological repeats of all four groups. I’m not sure if this is enough and if later on just compare each gene between groups with unpaired ttest to find target genes. Also I’m not sure if I need more than one sample per group and repeats for the same samples. Feel free to let me know if you need more information or any comments on this. Thank you so much.
22 November, 2016
Sarah Osmundson
I have analyzed my data for a project but need assistance deciding whether/how to make cut points and how to interpret the data. These are data from a prospective cohort study examining opioid use after hospital discharge. Most women use much less than prescribed but I am trying to make sense of women who use more than prescribed. Unfortunately due to my clinical schedule I cannot come on Mondays or Wednesdays.
Benjamin J. Reisman
David (cc’d) and I would like to attend the 11/22 biostats clinic to review our flow cytometry data and discuss the best way to generate “flowcytometry like” data to test out some of our analysis scripts and optimize our experimental setup.
The problem we’re trying to solve is that cell staining appears to be dependent on cell size, as reflected in FSC/SSC. We’re attempting to barcode cells by staining cells form each well with a different level of fluorescent dye, which allows us to combine the wells, run them together, then resolve which well they originated from our experiment (ref:
https://www.ncbi.nlm.nih.gov/pubmed/21207359)
Due to the correlation between cell size and staining, the largest cells in a low level may be brighter than the smallest cell in a higher level. I’d like to optimize the concentrations of dye we’re using modeling, but I need to generate model datasets to test on. If I have an experiment with cells of known FSC/SSC and Dye 1, how can I generate data with similar distributions covariances? Thought it might be most straightforward to subsample from our real data but was looking for expert input on how to best go about this.
25 October, 2016
Aaron Lim
Topic: Analyzing RealTime PCR data from multiple plates
Name: Aaron Lim
Affiliation: Vanderbilt Medical Scientist Training Program
Mentor: W. Kim Rathmell, M.D., Ph.D.
Dept: Medicine
I would like to attend the Tuesday, Oct. 25 clinic to discuss how to analyze RealTime PCR data from multiple plates, and how to compare gene expression from multiple tumor samples.
Carolina Pinzon
I would like to attend tomorrow again to double check some calculations. Thanks
11 October, 2016
Carolina Pinzonguzman
I would like to attend the clinic on Tuesday Oct 11.
I am planning on running a proteomic study on amniotic fluid. This assay has never been done before on amniotic fluid in the second trimester of pregnancy. I am applying for VICTR funds and they are asking me for a sample size justification. I am planning on running 6 samples (3 control and 3 test)
Thanks
27 September, 2016
Tomorrow, Tuesday September 12th, I am planning to attend the Omics Data Clinic to get some help with some mass spectrometry data that I have. I have two sets of data from two similar groups of patients which I would like to compare to one another. I have had some trouble doing this and my analysis is complicated by a few different things. Will there be a statistician present who can help me?
13 September, 2016
Shambnam Sarker
I would like to reserve a spot on Tuesday 9/13/16 noon to discuss my research which is a diagnostic crosssectional study that I soon begin data collection on and appropriate analysis.
Consultants: Yaomin Xu, Alex Zhao
23 August, 2016
Zachary Dubit
Ayan Mukhopadhyay
I am a third year PhD student in the department of Computer Science, working in the Computational Economics Research Lab. My advisor is Prof. Yevgeniy Vorobeychik. My research interest is primarily Machine Learning, and I specifically work on predicting crimes and other emergency events. I need some help in understanding and answering a few questions about Survival Analysis. Tuesday looks like the best fit regarding this but I can attend on other days as well.
16 August, 2016
Zachary Dubit
I was wondering if it would be possible to attend the clinic on Tuesday, August 16
^{th}at noon in MCN for assistance with a statistics project using R and binary logistic regression. Thanks for your help and for offering this resource!
Sarah Kleiman
2 August, 2016
Consultants: Hakmook Kang
21 June, 2016
Heidi Silver
Consultants: Hakmook Kang
I'd like to use the IDIOM data to look at changes over time in glucose, insulin and cpeptide, and the various indices of insulin sensitivity and resistance.
10 May, 2016
Dara L. Eckerle Mize
I am hoping to get a little help with multiple imputation in R. I am using aregImpute on a small dataset (10,000 observations of 2 predictors) but I am not completely sure if I am doing it correctly. Please let me know if this is not something that can be discussed.
Benjamin K. Poulose, Associate Professor of Surgery
(at 1pm) Sampling methods to perform long term followup in a surgical registry.
26 April, 2016
Julian Peters
I need some help on diagnosis and prognosis data that I have collected. This data is from a longitudinal study collected at various time points during treatment for each patient. I would like to create trends in the data by each diagnostic method and then to determine if the 3 diagnostic methods are predictive of each other, if they correlate and to what extent they correlate. I would also want to determine if these diagnostic methods are predictive of treatment outcome.
12 April, 2016
Lucy Spalluto
I am working on a faculty development project. We are assessing the utility of educational modules. An anonymous premodule survey with yes/no questions is sent to faculty members. An educational module is presented (not all faculty attend, material is made available online for review to everyone). An anonymous postmodule survey with the same set of yes/no questions is sent. Two additional questions ask if they attended the event and whether or not they reviewed the educational materials.
28 March, 2016
Eric Rellinger
Consultants: Yaomin Xu
I am a research fellow working in the laboratory of Dr. Dai Chung. We have been collaborating with Dr. Beauchamp on evaluating the effects of his compound ML327 on neuroblastoma growth. We have had a pretty robust phenotypic response and are planning to perform RNA sequencing of neuroblastoma cells treated at early and late time point to 1) identify potential upstream regulators, 2) identify potential compensatory pathways that mediate cell survival in the presence of ML327, and 3) determine whether the fate of these cells are altered in the presence of the compound (i.e. differentiation). I reached out to Dr. Zhu and she thought that you might be a useful resource for biostatistical support for this endeavor.
I have written a VICTR grant to help support the funding of this endeavor and was curious if you would be willing to aid with the biostatistical interpretation of those results. VICTR submissions require a biostatistician before submitting the proposal. In total, we are planning to have 8 different groups which will be completed as biologic triplicates, resulting in a total of 24 samples submitted for 30M 75bp paired end reads.
Any thoughts or feedback would be greatly appreciated. I would be happy to meet as well if you wanted to discuss the project and its goals in further detail.
Best wishes,
Eric
Comments:
1. Independent cell lines will be used for measurements at two time points.
2. Suggest to identify genes based on foldchanges comparing each pairs of conditions and use 2 foldchange as a cutoff. Given relatively robust phenotypic response and expacted effect size, three replicates at each experiemntal conditons (24 samples in total) should provide sufficient power in this discovery project to generate preliminary results.
Flavio Silva
My name is Flavio Silva and I am a physical therapist with the Department of Orthopedics. I just finished data collection for a case control study about Injury prevention for musicians: SCAPULAR AND CERVICAL NEUROMUSCULAR DEFICITS IN MUSICIANS WITH AND WITHOUT PLAYING RELATED MUSCULOSKELETAL DISORDERS: A CASECONTROL STUDY
IRB NUMBER: 141569
I was wondering if I could get some assistance with the analysis. The project is through the department of orthopedics. The data set is very simple but I need some help with running a regression and with descriptive data set.
02 February, 2016
Kimberly Albert, Vanderbilt University
I would like to come to a walkin clinic for help in planning the analytical plan for the study described below for a grant application. I am happy to come on a different day if Thursday is not the appropriate topic.
Agematched older men and women will be compared. Cognitive performance will be quantitatively assessed at screening and following a psychosocial stress task. A one year followup assessment will be completed to examine the predictive value of neural activity during psychosocial stress on longterm cognitive status.
Aim 1: Examine sex differences in psychosocial stress induced changes in acute cognitive performance and functional connectivity in older adults with Subjective Cognitive Decline (SCD).
Aim 2: Examine whether neural activity during psychosocial stress in older adults with SCD correlates with longterm objective cognitive performance change over one year.
19 January, 2016
Akshitkumar Mistry, MD, Vanderbilt University
I would like to review metaanalysis techniques. I have conducted a metaanalysis correlating brain tumor location with survival. However, survival in brain tumor is also dependent on other variables such as age, extent of surgical tumor resection, etc. My metaanalysis shows a statistical signficant effect of brain tumor location with survival; however, I do not know how to account for the confounding variables (age, extent of resection, etc...). I would like to show you the data and review statistical techniques so that I can apply for a VICTR voucher.
Charles Caskey, PhD, Vanderbilt University
Discuss collaborating with someone on an upcoming grant submission
12 January, 2016
Sandeep Arora, MBBS, Department of Radiology and Radiological Sciences, Vanderbilt University
 I was hoping to attend a clinic to discuss a grant submission for the following project  submission deadline is Jan 15 (next tuesday will be great). Abstract  attached. I need to discuss power and statistical analyses methods.
 PhaseShift Nanodroplet Assisted Multifocus MR guided Focused Ultrasound Ablation of Lobar Portal Venous Supply and Ipsilateral VX2 Hepatic Tumor Implants in a Rabbit Model.
15 December, 2015
Karthik Sundaram, Vanderbilt University
 I'm currently at Vanderbilt Radiology Resident looking to work on an imaging project related to imaging prostate cancer in patients undergoing a prostatectomy and we could use the biostat department's help in calculating costs.
 Briefly, we plan to image patients with prostate cancer that overexpression of a receptor called the organic anion transporter. Evidence suggests that 50% of prostate cancer patients overexpress the receptor. We plan on staining for the receptor post resection. Based on this information, we would like the clinic's help in calculating the number of patients in our pilot study that we need to image in order to have success in our study.
08 December, 2015
Chan Gao, MD. Ph.D., Physical Medicine & Rehabilitation
Consultants: Yaomin Xu, Run Fan
I will undertake a research project "genomewide association study of rotator cuff tear". I am submitting application for
BioVU access. The stat analysis method needs to be described:
Briefly describe the method for and statistical analysis.
Include sample size estimation, dependent/outcome variable(s), independent variables (include SNPs, covariates, confounders), type of statistical model (if appropriate), how SNPs will be coded, power calculation/ population stratification plans.
 Gave a very detailed overview about
BioVU. Refer to Dr. Todd Edwards for more support.
15 September, 2015
Matthew Rioth, Vanderbilt Ingram Cancer Center
Consultants: Yaomin Xu
 I am proposing an investigation of the association of observed tumor genetic variants and time on treatment phenotype. Specifically, we have a database of variants in ~800 tumor samples detected by exome panel sequencing (~300 genes, median 12 variants per sample). We have the ability to extract treatment information from the EHR. We would like to correlate variants, grouped by protein functional region (ie ligand binding domain, DNA binding domain, catalytic site, etc) with treatment response.
 A hypothetical discovery could be that patients with mutations in a linker region in the estrogen receptor gene have much shorter time on treatment with fulvestrant (an estrogen receptor antagonist) relative to mutations in other regions of the gene. This implicating mutations in that region as a mechanism of fulvestrant resistance. Since we will be able to extract time on treatment information from the EHR, the analysis we were proposing would be a Cox (time to event) regression. Does this sound appropriate? Is there a lower limit or guidance for how few patients to have in a group in this analysis? If we were to perform this analysis for multiple phenotypes (hypothetically 1020 phenotypes) associated with multiple genotypes, what kind of multiple hypothesis correction should we use?
30 June 15
Ping Wang, VUIIS
5 May 15
George Nelson, MD Assistant Professor of Medicine, Division of Infectious Diseases
Consultants: Fei Ye, Pengcheng Lu.
 * I have a quick question about power calculations for a noninferiority cross over trial. I will bring all relevant numbers
 Rate of MRSA Acquisition data: This would be a cluster randomized cross over trial with standard of care being isolation of those with any MRSA isolate and intervention being isolation of only draining MRSA wounds. The intervention period would be 3 months with 2 week wash out and cross over to standard care (control)/intervention status. Literature reports 3.55/1000 patient days acuisition of MRSA in clinical care. WE have ~ 6 units available for study with 15000 patient days over 3 month period.
14 April 15
Jack Virostko, Research Assistant Professor, VUIIS
7 April 15
Gabrielle Rushing, student, Neuroscience Graduate Program
Consultants: Fei Ye, Hakmook Kang, Pengcheng Lu.
 Analysis of flow cytometry data: expression of different phosphoprotein levels in neural stem cells
 Compare region to region variations and variaion between three mouse strains.
 Paired comparisons and multiple group analysis: Wilcoxon signed rank test (paired) and Kruskal Wallis test (multiple groups)
 More complicated correlated data analysis: mixed effects model with appropriate number of samples.
17 March 15
Akshitkumar Mistry, resident, neurosurgery
 Retrospective study of 57 patients who underwent surgery for trigeminal neuralgia on one side of brain and whose MRIs on both sides were later examined. Two factors were examined: (1) degree of contract (none, contact, compression) (2) location of vascular contact (at REZ and distal to REZ).
 Hypothesis: location and degree of contact are associated with the side of surgery (each patients had information on two sides and only had surgery on one side). Mixedeffects logitstic regression model on surgery (yes/no) on the two factors.
 Hypothesis: location and degree of contract are associated with early outcome (0, 1, 2, 3) on surgical side. Ordinal logistic regression model on the two factors.
 Suggest applying for $4000 Voucher to perform the statistical analysis and prepare the manuscript.
10 March 15
Attendants: Poojitha Matta, and Mentor: Stacy Sherrod, PhD Department of Chemistry.
Consultants: Fei Ye, Steven Chen, Pengcheng Lu.
 My project aims to characterize the impact of chorioamnionitis (a maternal infection contracted during pregnancy) on the fetal immune system.
 Two group of different patient samples, one is Chorio + and Chorio  (i.e. infection before birth in mothers), 10 samples in each group. Within each group, the patients were in two categories: one was control, the other was activated by Acid B and CD28. There were ~1600 features with normalized metabolites intensities.
 Sample size is too small. The analysis will be underpower. Two factors, both were binary variables, and there were correlation issues as well due to the measurements on same sample.
 The simplified linear regression model: Activated ~ Control + Chorio + error term.
 Another naive way: Use the difference between Activated and Control as outcome measurement, compare the difference between two populations. Don't report p values.
20 January 15
Attendants: Antonio Hernandez, MD, Liming Luan, PhD, and Edward Sherwood (Mentor), MD/PhD Department of Anesthesiology.
Consultants: Fei Ye, Steven Chen, Yaomin Xu, Pengcheng Lu and Li Wang
 Our lab has been involved in a study of challenging human neutrophils with either lipopolysaccharide or Monophosphoryl Lipid A, for the evaluation of inflammation. We looked at gene expression for inflammation. We have the data, but we are unclear as to how to proceed. Specifically, how to analyze the data and whom to approach for assistance.
 Experimental Design: 18 people's blood samples. Six samples with PBS, six samples stimulated with LPS, and six samples stimulated with MPLA (monophosphoryl lipid A)
 Suggestion: DE analysis, Gene pathway analysis to cluster genes, and gene set analysis (GSEA).
 We prefer getting the raw data, .CEL files, to start the processing to assure the quality of normalization.
 Contact Steven Chen for further collaboration.
13 January 15
Attendants: Carolina PinzonGuzman, MD/PhD
 I'm a PGY2 surgical resident writing an IRB and VICTRL proposal but I am stuck on the statistical part trying to figure out how may patients I need for my study. I would like to talk to somebody about it. I can go to the clinic tomorrow, or I can meet somewhere else. It is a simple question. I am looking at the amniotic fluid in pregnant females with babies with congenital abnormalities and comparing the amount of some growth factors in it vs control amniotic fluid. Nobody has ever done this experiment and I am not sure we would be able to find a difference.
 How do you predict how many amniotic fluid samples I need?
 What if I want a do discovery proteomic study looking at a difference in protein amounts in the amniotic fluid?
 Note: Showed up at 12:50pm, she decided to come to tomorrow's clinic.
Attendants[Stop by client]: Manisha Gupte, MD. Cardiology
Consultants: Dan Ayers, Hakmook Kang, Pengcheng Lu, Alex Zhao
 Question: A Pilot study with four ECHO treated samples and three untreated/control samples, outcome is continuous measurement of activity at week 0 (baseline), week 1 through 7. Any suggestion on group comparison for treatment effect?
 Not statical testing of comparison is need ed for pilot study especially when sample size is too small.
 Try to find the minimum biological difference you would like to detected.
 Define your time points, why did the experiment end at week 7 rather than week 9?
 Compare the data at last time points make more sense than doing it at each time point.
 Need to adjust data for baseline.
 Contact Frank Harrell if statistician's help is needed in the future.
6 January 15
Attendants: Dr. Adrienne Dula
Consultants: Hakmook Kang, Li Wang
 I am an imaging scientist and am working on a VICTR application.
 Ironbased new contrast agent for liver imaging, enhancing T1 & suppressing T2 compared to current contrast agents. This can be metabolized by the liver.
 Diagnosis for patients with liver diseases & with chronic kidney diseases
 Current way for diagnosis is still noninvasive but not applicable to whom have both liver and chronic kidney diseases  Aim 1.
 Recruit patients with known liver cancer and no kidney disease: can get ferumoxytol (new agenets). Step2, search their previous MR images (within a month) with gadolinium (Gd)  based contrast agent (old, gold standard for now). Compare the new enhanced images with the corresponding standard images. Variables: size and number of tumor.
 Aim 2: Fibrosis > Cirrhosis: Invaisive method and not reliable. New MRbased imaging tool for noninvaisive and reliable quantification of the degree of fibrosis > build prediction model at the end.
 $10,000 pilot VICTR pilot project (matched by John Gore, sum to $20,000)
 $267 ferumoxytol per person + $500 per hour for MRI ~ $850 > 23 subjects.
 Power analysis based on this number > claim that this sample size is capped by the budget and maybe underpowered.
 $2000 (35hours) biostat voucher is recommended.
16 December 14
Attendants: Dr. Jane Ferguson
Consultants: ZhengZheng Tang, Guanhua Chen, Pengcheng Lu, Li Wang.
 I am hoping to get some advice on analyzing and integrating multiple highthroughput datasets, with the primary aim of developing a coherent analysis plan for a grant submission. Based on the information on the website, I believe that the Tuesday clinic for highdimensional data may be the most appropriate. I work with human samples, and am proposing to integrate multiple levels of omics data in ~100 healthy humans. I will have data on gut and oral microbiome composition (sequencing data, collapsed into 100’s of bacterial genera), gut and plasma metabolomics data (100’s to 1000’s of metabolites), plasma circulating microRNAs (100’s), as well as dietary information. I am submitting this grant in January, and would very much value input on the statistical analysis strategy.
 Question: Whether diet will affect the microbiome and metabolomics data;
 Suggestion: First, lay out all the hypotheses, which one is the most important one? Enough for R01, better to limit a bit of the study aims;
 Aim 1: Association between microbiome and diet and health markers; Find common signatures or compositions between same person's gut and oral? May separate them and do not do the comparison at this point; Aim 2: association between microbiome composition, diet and metabolomics markers; Aim 3: Identify miRNA markers associated with diet and microbiome;
 Software and Statistical Methods: 1. The HuttenHower Lab developed a series of software. 2. Hongzhe Li's group at UPenn; 3. Can model the associations by considering microbiome as dependent variable (predictor variables: health markers, dietary data), and for dependent variable metabolomics (and lower level microRNA markers), treat microbiome as predictor variable.
 Contact faculty: ZhengZheng Tang and Guanhua Chen for grand proposal assistance.
11 November 14
Attendants: Drs. Lucy Spalluto & Rifat Wahab, Women's imaging
Consultants: Hakmook Kang, Steven Chen, Pengcheng Lu, Min Gao.
 Data: Questionnaire result/summary worksheet.
 Suggestion: Most of the questions can form a r x c table and be analyzed using Chisquared test. Dr. Kang is the primary statistician to collaborate with the Department of Radiology.
Attendants: Anne Kenworthy and PostDoc Krish
 My postdoc Krish and I would like to attend the clinic this coming Friday (11/14) to discuss some data collected from microscopy images. (It is not high thruput information so I felt like Friday would probably be more appropriate than Tues.) I am attaching an example of the data we are analyzing. We want to analyze the % of cells that contain tubules under different experimental conditions. We have also analyzed other tubule properties such as their length. We have some ideas about what statistical tests to apply this but would like asecond opinion to make sure we are doing this correctly.
 Suggestion: Poison regression model for count data by taking into account for the experimental conditions and offsets.
Attendants: Dara Mize
 I am working on a project for a Bioinformatics course and am attempting to perform some statistical analysis in R using 2 data sets from GEO. The data sets each contain microarrays for paired normal and papillary thyroid cancer tissues. I think I would like to compare gene expression between normal and cancerous tissue in each set individually. Then, I would like to analyze gene expression between normal and cancerous tissue when the two sets are combined. I would appreciate any feedback about this approach and have some specific questions about using R with these datasets.
 Suggestion: 1. Use Bioconductor package GEOquery to retrieve gene expression data; 2. Two data sets shared the same platform, but could not combine them directly due to different normalization methods and batch effect. 3. Paired and nonpaired ttest could be applied to two data sets respectively. 4. Need to adjust nominal p values for meaningful interpretation (FDR).
30 September 14
Attendants: Christy Pearce, Asst. Prof. Division of Maternal Fetal Medicine, Department OB/GYN
Consultants: Hakmook Kang, Yaomin Xu, Steven Chen, Pengcheng Lu, Li Wang, Xue Han, Min Gao and a student from Dan's Biostatistics class
 I am not able to come to a Thursday clinic, so was advised to come to a different noon clinic. I would like to sign up for 9/30. I have run some stats in JMP, but would like these checked for accuracy as well as another calculation. Also, I would like help in the BioVU application on this particular SD dataset. Thank you in advance for your direction and help. Please let me know what else you may need.
 I have attached my data as well a pdf's of the JMP analyses I ran. I also attached the abstract that I submitted for our annual SMFM meeting. This data was from the SD, so it is deidentified. Goal was to identify risk factors for cardiac dysfunction (defined on echocardiography) in patients with preeclampsia. Design is case control. Cases: preeclampsia + cardiac dysfunction, and controls: preeclampsia without cardiac dysfunction. Exposure is BP, creatinine, LDH, uric acid, etc. In the end, really is more crosssectional since the "exposure" typically occurs very close to the cardiac dysfunction.
 Questions:
 Are the analyses I ran accurate?
 Is there a regression analysis that can look at all the "exposures" and give a risk of cardiac dysfunction with additional "exposures" or way to make a scoring system based on exposures seen in each patient. So if a patient comes in with DBP >110 and CR >1 for example, her risk is _ as opposed to a woman who only has the elevated BP.
 Need help with sample size calculation to use this data set for GWAS data analysis as well as specific genes, including:
 PEE 1 chromosome 2p13;
 PEE 2 chromosome 2p25;
 PEE 3 chromosome 9p13;
 PEE 4 STOX1 gene chromosome 10q22;
 PEE 5 CORIN gene chromosome 4p12;
 EPHX chromosome 1q, EPOX, HYL1, MEH, EPHX1 (all known as epoxide hydrolase, microsomal [xenobiotic);
 NOS3 (ECNOS, eNOS, NOS3, nitric oxide synthase 3 [endothelial cell].
 Design: Based on the availability of the SD database, only 33 cases were obtained in this study, and 99 control samples were included(3to1 ratio).
 Suggestion and concerns: Regarding the JMP output, some concerns are: a few categorical variables need to be treated carefully with multiple levels and very small number issue, may combine some levels. Rethink lots of variables with large numbers (>75%) of missing values, could be excluded if they are not important in term of the research interest.
 Modeling strategies: Due to 33 cases only, three predictor variables can be included in the logistic regression model as a good statistical practice, but predictor variables could not be chosen based on the univariate analysis result (from JMP). If including variables based on literature of research interest, very likely there will be more than three independent variables, can think of the penalized logistic regression model, but the power is still a big concern of this study. Doing metaanalysis if possible by trying to find other similar research data sets outside to increase the sample size.
 It is not at the stage of sample size determination for SNP analysis yet, but obviously there is not enough case samples available in the SD db.
 Contact Dr. Chris Slaughter first to check if there is collaboration plan between him and the Department of OB. Since APS funding is available, feeforservice model will also work possibly.
26 August 14
Attendants: Scott McCall, MD/PhD Candidate
Consultants: Yaomin Xu(contact), Pengcheng Lu, Meng Xu, Alex Zhao, Derek Smith and Allison Hainline
 My name Scott McCall and I'm a graduate student in Billy Hudson's lab. I'm currently working on a project with Josh Denny involving abdominal aortic aneurysms and BioVU and I'm at a statistical impasse! I would like to schedule a time this week if possible. Based on the descriptions of the different focus of each day, I'm unsure which would be best.
The root of the problem is that there is a seemingly strong, nonlinear dependence on some covariates which I am having difficulty incorporating into the final regression analysis as part of my overarching story. After working with friends in Bioimformatics (Jacob
VanHouten and Pedro Teixeira), we have been unable to solve my problem of trying to incorporate splines into some of my regressions to capture these associations in an internally consistent way. So it is my hope that we can work on this as part of the clinic. In addition to the data set (as per the specifications on the wiki), I am including the initial draft of the figures so we can hopefully fold this into the discussion also .
 Suggestion: fit restricted cubic splint regression model to catch the nonlinear effect of age, say let pulse pressure be the outcome; nonlinear interaction between age and G allele dose levels needs to be tested before drawing conclusion of "age dependence of SNP effect"; Race may also need to be adjusted in the model if the information is available, which may improve the precision of estimation of the allele effect.
 Follow up with Yaomin for potential statistical collaboration.
22 July 14
Attendant: Spyros Kalams, Vanderbilt HIV Vaccine Trials Unit
 I would like to request some time for a VICTR biostats studio Friday 62614. Dr. David Aronoff and I are putting together a small study of 15 individuals. We are evaluating the effects of a drug on T cell activation, and we are comparing the effects of two different routes of delivery of the drug. I can go over this in more detail during the studio, but we would basically like to describe the way we will analyze the data in the study protocol.
 Primary outcome will be the T cell activation  percentage (continuous), which will be measured 4 times from day 0 to day 28. Descriptive statistics about data distribution like median and IQR can be calculated. Linear mixed effects model can be used to explore the activation change over time.
 Fisher's Z transformation or logit can be applied to make percentage goes from negative infinity to infinity.
 Count data can be analyzed using Poisson regression.
 If multiple biomarkers are processed simutaneously, pvalues need to be adjusted.
Attendant: Aliya Gifford, Graduate Student (Chemical and Physical Biology)
 I'm a graduate student here and would like to attend a biostatistics clinic, and if possible I'd like to attend either Tuesday, Wednesday or Thursday this week. The questions I have are in regards to regression analysis, and understanding how and what approach to take, given the measurements we have. If there's more information I need to supply, please let me know.
 There are total N=18 subjects with brown fat and N=5 subjects with nonbrown fat. Each subject underwent both CT and PET. There are also measures of FSF and R2s.
 Primary question: whether imaging results with BMI, Gender, Waist, Age, Height, Weight, and Temperature can be used to predict whether that patient has brown fat or not.
 logit (Y=brown fat) = CT + PET + FSF + R2s + demographics. Need penalization. Consider interaction between temperature and imaging.
 Current data is ROI based, can consider voxel based analysis.
 Scatter plot for all the possible pairs together with spearman correlation coefficient for descriptive analysis.
Attendant: Daniel Croymans, internal medicine
 Primary outcomes: number of hospitalization, job productivity, sick days, Dependent variables: BMI...
13 May 14
Attendants: Roop Gill, Plastic Surgery
Consultants: Bill Dupont
 Project looking at risk factors for rhinoplasty complications.
 There are two data bases. One is about cosmetic procedure (~18,000), and the other is the complication data (~2,500). Need to merge them together by some common variables.
 Will apply for a VICTR voucher and suggest $2000.
25 Feb 14
Attendants: Jennifer Herington and mentor Jeff Reese (not in attendance), Department of Pediatrics/Neonatology
Consultants: Pengcheng Lu
 Study objects: Identify novel inhibitors of intracellular calcium release from uterine smooth muscle cells.
 Method: Use highthroughput screening to measure changes in intracellular calcium release (indicated by relative fluorescent units) from mouse and human uterine smooth muscle cells (UTSMCs). We will base our analysis off of a publication from Prudencio Dianas Vol 2 No 1 2013. First, plates contain compounds run in singlet only, for quality control and to determine the threshold for antagonist activity (selection of drugs). A z'factor and coefficient of variation will also be performed for quality control (robustness of assay to use in highthroughput screening). Once we determine "hit" compounds, we will repeat the assay 3 more times, therefore increasing our Nvalue to 3 but this will still be too small for a ttest.
 Suggestion: We will use linear models and moderated tstatistics as implemented in Limma in Bioconductor to overcome the small sample size issue.
 The estimated time to process HTS data is about 6 hours.
5 Nov 13
Attendants: Parimal Samir, Andy Link, Chris Browne, Ryan Delahanty and Rebecca Levinson
Consultants: Lily Wang, Steven Chen, Pengcheng Lu
 Questions of Parimal Samir (from Andrew J. Link Lab: Dept. of Pathology, Microbiology and Immunology):
 Study objects: 1. Study the response of cells when its growth condition is changed from its happy physiology condition; 2. Find modules in the proteome that respond to specific changes in the physiology condition; 3. Find the combinatorial effect of the simutaneous changes in growth condition.
 Method: Compare 3 experiments and one control group, then classify the winner proteins(based on p values from ANOVA tests) into classes(based on FC>1.5 and p<.05 from Ttests). Totally 12 clusters were expected.
 Suggestion: Fisher's exact test and KolmogorovSmirnov tests both assume all proteins are independent, this could be incorrect, can use GSEA. They updated the db recently and can input gene symbol as well.
 Questions of Ryan Delahanty and Rebecca Levinson (from Epi / Human genetics division)
 Question: logistic regression to test the combined phenotype, genotypes.The outcome is disease/condition, there are 1600 different medical conditions, predictors are covariates like age, principle components and CNV(~1000 CNVs, coded as 0, 1, 2, NA). It's a kind of "phewas" study. The sample size is around 3000 patients. Would like to permute on CNVs, but the problem is when NA presented in CNVs, the outcome will change.
 Suggestion: Using FDR to adjust the p values from 1000 tests, you can use qvalue as well; can also try exact logistic regression to get the exact p values.
20 Aug 13
Rockann Mosser, postdoc, and Maureen Gannon, Division of Endocrinology, Diabetes, and Metabolism, Dept. of Medicine
Dr. Dave Tabb told me to contact you (and ask for Ming Li if available) for some statistical help with some protein mass spec data sets I have. Basically, I have 2 studies: one with rat serum and one with mouse serum. The rat study compares 4 sets of rats (each set with an n of 3): 2 month old control, 2 month old treated, 6 month old control, and 6 month old treated. The mouse study compares 2 sets of mice again with an n of 3): control vs treated. I have quasitel.tsvfiles comparing age matched rats and the mouse set (so 3 comparisons, so far). I want to do more complex comparisons / statistics with these data sets, but I am not sure how best to proceed or even if the quasitelstatistics are correct for the comparisons I have (in other words, what I should be looking at).
13 Aug 13
Carrie Moore, Graduate Student, CHGR
To discuss penalized regression, particularly Lasso/Ridge/Elastic Net analyses. I have a couple of scripts in R which utilize glmnet and caret packages to perform elastic net analyses. In one of the scripts, some of the predictors which I know to be highly correlated with the outcome are not showing up in the final models. Is anyone in the biostatistics department with knowledge of elastic nets or those particular R packages available to answer a few questions?
 Consider the purpose of this model. Is it prediction or selecting relevant variables?
 Try running the procedures on your entire dataset and see how the result compares to those of your CV/bootstrap programs
 Consider using bootstrap internal validation
 Bootstrap and cross validation are useful for telling you how well your model performs, not for selecting variables.
 The major purpose of the cross validation is the variable tuning for the elastic net parameters (alpha and lambda).
 Have 180 individuals with about 90 events. Since there are 15k candidate predictors, the dimensionality is really too big.
 Sure independence screening: two stage method. First stage looks at correlations of the vars with the outcome. This uses cross validation. Stage two is something like lasso or elastic net.
 Random lasso. Discussed that the interpretation of the results would be difficult.
 Consider using ridge regression since it has only one tuning parameter, whose estimates will be more stable. To select the lambda, try bootstrapping the process. IF the lambda parameter does not vary much, then you might avoid the bootstrap. Then in the resulting model, could try "model approximation" where you fit are really nonparsimonious model and then try selecting a subset of the variables that represent about 90% of the signal. Can do this with a stepwise selection process that is blinded to the actual outcomes, but instead uses the predicted values from the model fit.
 see optimism bootstrap
 Consider forcing FEV, age, and height to be in model. Since you know they are important, consider using splines to allow them to behave nonlinearly.
Georgia Wiesner and Kelly Taylor, Department of Medicine
 genetic testing sometimes reveals actionable mutations and variants in genes that were not part of the reason for the testing.
 ACMG has a policy stating that there is a group of genes variants for which, if there is a discovery, the information should be passed to the clinician. Some of them are in cancer and some are in cardiovascular health. The question of interest is with identifying these variants in children.
 Planning a VICTR funding submission and looking for input on the statistical analysis plan.
 Part of the study's goal is to build an anonymized data set in biovu to assess the frequencies of deleterious mutations.
 Need to know the sample size.
 Calculate the sample size needed to achieve a given precision in the estimate of proportion. Find the maximum prevalence level you are likely to see, and base the calculation on the maximum. This will be the "worst" case in terms of precision.
 There is a fivelevel rating scale to categorize the prognosis for different variants, ranging from deleterious to uncertain to beneficial.
 Consider getting the frequency of deleterious variants separately by function or by population groups.
25 June 13
Cody Wenthur, Pharmacology : Peter Martin(Mentor, Psychiatry)
Statisticians: Pengcheng Lu, Yuwei Zhu
 Cody is a Ph.D. student of Dr. Craig Lindsley, and Dr Peter Martin.
 Project: Measurement of variation in metabotropic glutamate receptorencoding genes (GRM3) amongst substancedependent individuals: Implications for development of novel therapeutics
 Used CaTS softare to determine power, recommended use of PGA software with additive design, and matched study design
 Recommended attendance at VANGARD design studio for sample size verification and power calculation along with data analysis plan
 Roughly estimated hours for VICTR grant: 60 hours (analysis and manuscript preparation)
14 May 13
Katherine Betke, Pharmacology
Statisticians: Pengcheng Lu, Hakmook Kang
 Katherine is a Ph.D. student of Dr. Heidi Hamm. Her project is to compare presynaptic and postsynaptic protein expression enrichment data.
 G protein expression data of six mice was collected in both pre and post regions, the null hypothesis is there's no difference of protein expression data between two regions.
 Check data distribution to see if logtransformation is needed; Can do paired ttest, but Wicoxon signed rank test is preferred.
 With logtransformation, the logFC will be the mean difference of expression data, zero means no change and FC is 1.
 Data visualization could be either scatter plot or bar chart. For bar plot, can add either error bar or 95% CI to show data variations.
 Will do for >20 proteins. Then multiple testing correction is needed. Bonferroni or FDR method could be used, but Bonferroni is too conservative.
 In R, p.adjust() can do p value adjustment by specifying methods.
7 May 13
Leslie Halpern, MMC
I have a project based upon previous work that I did. It is a diagnostic protocol using injury location and a screening questionnaire to diagnose victims of violence/abuse. I am wellpublished in this area. I am now involved in a project using this protocol with another well statistically used questionnaire and using salivary markers to measure inflammatory peptides in victims as compared with controls. I need to go over the use of my multivariate model to include the salivary sampling. I also need to discuss the power needed for this pilot study since finding is small and I need to but kits for the sampling. There are 3 other investigators in this project including: Johnson, Desiree; Gangula, Pandu R.
23 April 13 (probable)
Daniel Cohen, Pathology Resident
To discuss my project in anticipation for a VICTR voucher for a BioVU Exome Chip comparison study to identify SNPs for a patient population of interest. With this and other genes identified I will pursue NGS sequencing of patient cancer tissue and VICTR grant to fund in part the sequencing. Attached is an abstract relevant to the study. I have discussed this study with Dr. Yu Shyr. The introduction and services from your department were suggested from the VICTR office. I request your guidance with Methods 4 and 7 from the abstract: 4)
BioVU Exome CHIP analysis of otherwise healthy nonBRAFi patients with cSCC (233 pts as of 2/2012)and age, gender, race matched healthy non!cSCC controls to assess SNP association frequency with cSCC not present in normal individuals. Perhaps 699 BioVU patients could be extracted for control (3x)? 7) Biostatistics association analysis of lesion histology (verruca, AKs and BRAFicSCC of each morphologic subtype), HPV infection rate and hostgenotype from NGS data of ~307 skin lesions from 55 patients (see table "...pt summary".
 Material has been placed in the clinic room main computer in the
clinic/hddata
folder
19 March 13
Stacy Sherrod, Physics and Astronomy Department
Consultants: Ming Li
Project: Metabolomics Mass Spectrometry Data
 The types of samples and data: Copied from Stacy's email:
What types of samples I have…
 Typically a control, load, quality controls and a few treatment types (in the data I have attached, I have control (C), load (LOAD), quality control (QC), astrocytes (A), microglia (M), neuron(N))
 Important: Loads and Quality Controls are the same sample, ‘Load’ samples condition prior to running any other samples, typically 10 injections, the ‘quality control’ samples are ran every 10 samples. Both Load and QC samples should group together… and should account for instrument variability over the course of all the runs.
 Both ‘Load’ and ‘Quality Control’ samples are an equal mixture of all samples in the sample set.
 Sometimes I’ll have a time course and different treatments.
 I try to do 3 biological replicates, BR) of each sample and 3 technical replicates of each, but don’t always have enough sample for that, or misinjects of the instrument so that number changes.
I typically use XCMS (in R) to filter and do a retention time correction of all my samples, afterwards I normalize the signals.
With all that being said – this is what I get after analysis…
 m/z
 Retention time (from liquid chromatography), though not always, depends on the experiment that is being performed
 Normalized signal for each m/z
 Goal: "In the end, I just want to know what RT and m/z differ across samples and which samples they differ against (A vs. C, A vs. N, etc.) so that I can work on identifying those peaks. Maybe an ANOVA would work since I have multiple groups…."
 Statistical Inputs at clinics:
 When acquiring the data from mass spectrometry, make sure your samples are randomized;
 Assess the quality of the data: here are some possible ways to check: calculate CV within each group to check the "single to noise" ratio of your data; calculate the ICC to check the reliability/reproducibility of your measurements; apply clustering techniques to visually check if you can cluster all the control sample together;
 For data with correlated structure (biological replicates, technique replicates), apply linear mixed effect model;
 Clarify the goal: if the goal is to find individual "retention" time that can be a potential markers, univaraite analysis; if the goal is to do the prediction, may consider multivariable analysis, for example lasso based modeling techniques.
4 Dec 12
No clients
6 Nov 12
Ana De Lucas, Ophthalmology
 96 well plates, EPO, proliferation assay; controls not placed randomly on plates
 Cell proliferation (more fluorescence)
 Increasing concentration of EPO  4 levels
 Wild type vs. mutant
 24h and 48h; one plate per time point
 Standard ANOVA is for comparing two or more experimental conditions applied to independent experimental units
 Assess quality of experiment by looking at variation across replicates; can use intraclass correlation coefficient (ICC) for this, or other measures
 Independent wells of cells for each time, concentration, genetic group
 2x4x2 ANOVA, i.e., ANOVA with 3 factors (sets of conditions)
 ANOVA will not handle concentration trend efficiently
 Could assess Spearman rank correlation between concentration and cell growth
 Ordinary parametric ANOVA Ftests are not necessarily robust to strange data
 If time is allowed to interact with the other factors, this is equivalent to a separate analysis by 24h, 48h
 If do separate analyses by time, this is a 4x2 ANOVA if you did not use the ordering of concentrations
 Main question: is mutant resulting in more proliferation than wild type
 More specific: what is the difference in proliferation between wild type and mutant as a function of EPO concentration
 4 estimated differences with confidence intervals  do this in context of overall 4x2 ANOVA, i.e., 4 contrasts with simultaneous confidence limits to control overall confidence coverage of 0.95
 One estimate of variability (residual variance) from a unified ANOVA
 May be problem with lower limit of detection
 See http://www.genequantification.org/malodataanalysis2006.pdf
30 Oct 2012
Dehui Mi, HTS, VICB
Consultants: Bill Dupont, Uche Sampson, Frank Harrell
I have no data set to send. What I need is someone clearing out my confusion in some very basic concepts, such as standard deviation, z score, B score, normal distribution, the difference of population and sample, etc. You can help me to clear out my confusion in these concepts so that I can talk about them correctly in my presentation.
 High throughput screening, 384 well plate
 How to identify a "hit" based on "very different", e.g., 23 SDs away from the mean
 SD is a measure of dispersion (variety); average absolute difference between two observations is a more intuitive concept ( Gini's mean difference)
 SD is the square root of the variance and is on the original data scale
 n1 is used in the denominator instead of n to give a penalty for having to estimate the center (mean) of the distribution; result is an unbiased (i.e., right on the longterm average) estimate of the population variance
 The sum of squared differences is minimized when computed from the sample mean so it is optimistic when estimating the true variance
 Step 1  primer screen  compare every single well to all the test wells on the plate
 Step 2  confirmatory screen  compare wells to control
 Beware of methods that are tightly tied to the normal distribution
 Mean and SD are not robust statistics, i.e., a small number of "strange" values can distort their values
9 Oct 2012
Andy Link and Parimal Samir
To discuss our approach using semisupervised learning (SVM) to validate peptide identification from mass spectrometry experiments. We are developing a novel targetdecoy strategy. Decoy peptide hits are labelled as incorrect and target peptide hits are labeled as correct hits. The classifier is trained on a dataset containing both decoy and target hits. Finally, the trained classifier is used to validate the peptide identifications. We would like to discuss if our approach is statistically sound and get back your feedback.
Background
 most of yeast do not have much isoforms, mostly one gene, one protein
 isoforms  how exons are put together to make the final transcript
 yeast genes also not have much introns
 transcription factors can be far away from the gene it is modulating
 proteomics are most useful for figuring out protein interactions and modifications
SEQUEST
Goal: shotgun dataset, proteins are cut into peptides, to figure out what the peptides are, what the proteins are
Input: (1) precursor M/S ratio: MS scan  measures the mass to charge ratio, a measure of how well the peptide ionizes (2) MS/MS  a list of fragmented ions
Software: compare observed (1) and (2) values with theoretical values in the database
Proposed algorithm:
 hybrid of SVM and filtering
steps: (1) data cleaning: calculate centroids of targets and decoys, remove portions too close to decoys (2) SVM1: (3) SVM2: (4) filtering
Comments:
 "cleaning" training data may actually lose power for prediction on "uncleaned" test sets
 may want to define the utility function  a function of sensitivitiy, specificity and cost of misclassification
 ultimately only performance on test datasets matters, a classifier can perform superbly on tranining data but only ok on the test data
2 Oct 2012
Jennifer M. Giltnane, Dept. of Pathology, Microbiology, and Immunology
I have data structure/analysis question regarding how to best demonstrate change in endpoint marker (tumor proliferation before and after treatment) and compare it to an experimental finding (mutation). Data set is saved in ~/clinic/hddata.
Jun Dai, Epidemiology
25 Sep 2012
Qiuyun Fan, BME PhD student, affiliated with VUIIS and Education and Brain Research Lab
I'm working on one of the Kennedy Center sponsored projects. I have a dataset where I want to determine what factor(s) (targets 168) are important predictors of group/category difference. I first used PCA to reduce dimentionality, and then used linear regression to regress group membership onto the PCA scores. I then translated the beta in the PCA domain to original factors' domain to get an interpretable result. When I did linear regression in PCA domain, Matlab returned the confidence interval for each element in the beta. I'm wondering if I can translate the values of confidence interval to the original factors' domain as well.
 response: good reader vs. poor reader; better to use continuous score. Group is defined by a cutoff of continuous score, and subjects in the buffering zone are removed (this could cause bias).
 Predict brain function from brain structure
 40 subjects (after 15 patients removed), 68 variables(neuroimaging pixel)
 5 PCs explain 90% of variance
 Could explain the meaning of individual PC by examining the loadings.
 Examine the correlation between the variables and PCs
 SE of the coefficients of the original variables could be very big due to high correlation between the variables.
 Could group the original variables based on previous knowledge. Variable clustering based on correlation.
Doug Johnson, Hematology
 Does geno type affect the response to immunotherapy? Sample size justification
 Two variants of Ras
 Ras variant patients respond better than wildtype patients
 20 Ras (9 responded), 31 wildtype (6 responded)
 Another endpoint: survival to death, or survival at one year
 What's the feasible maximum sample size attainable? At what time point will the response be observed? Confounders in retrospective study (Ras patients maybe more likely to get treatment)
 Obtain the information on the minimum detectable difference (in response rate) from previous literature
 Make power curve
18 Sep 2012
Consultants: Bill DuPont, W. Wu.
Joe Solus, Medicine
Seeking help in R01 grant development relating to miRNA arrays. Referred to A. Shintani, W. Wu.
Quinn Wells
GWAS SNP study for association with reduction in EF following chemotherapy.
 EF measurement modalities:
 Nuclear  more precise
 Echo  less precise, often censored recording (i.e., "greater than 55")
 Cumulative chemotherapy dose is related to EF drop. If EF drops due to toxicity, chemotherapy is stopped.
 Idea 1. Use a protocol to identify "cases" and "controls", based on the magnitude of drop in EF
 Idea 2. Subset on patients measured via nuclear technique, regress drop in EF onto SNP data.
28 Aug 2012
Susan Kroop, Rheumatology
Consultants: Bill Dupont, Dan Ayers, Frank Harrell, Ming Li, Bill Dupont
I am beginning an education project on housestaff rheumatology curriculum change. I am at the stage of creating my surveys for data collection and want to make sure the design of the surveys will give me the data that I want/need.
 Interested in residents' attitudes, knowledge, skills
 Survey before/after 1w rotation (n=45/y); compare one year with next year
 Main question is whether questions as composed will yield analyzable data
 Questions have not been previously validated
 Using REDCap analog scales mainly
 Advantages of brevity
 Think about biases caused by prepost design (worst case: residents answer the way they think you want them to answer)
 Suggest talking to Irene Feuer
 Have multiple people look at draft questions
Thomas DiSalvo, Cardiovascular Medicine
Consultants: William Wu, Bill Dupont
Working on VICTR proposal looking at microarray gene expression in human right ventricles (RV) from explanted human myocardium. We are interested in comparing human RV gene expression (which has never before been studied, believe it or not) in 12 explanted heart failure hearts all of which have endstage LV failure (hence the transplant...) obtained at time of transplantation and also in 4 control hearts (nonused potential donors). Our sample sizes are "fixed" by the number of prepared samples  12 cases, 4 controls. All 12 cases have endstage LV HF  hence the transplant. We've completed a proteomics study in the same hearts, so are limited to this number of samples to do correlative genomicsproteomics.
In this pilot study, we'd like to look at the microarray data with 4 possible aims:
 Does RV gene expression differ from LV gene expression in normal explanted hearts ? 4 control RVs vs. 4 control LVs
 Does RV gene expression differ from LV gene expression in endstage LV HF explanted hearts? 12 pooled case RVs vs. 12 pooled case LVs
 Does RV gene expression differ by etiology in endstage LV HF explanted hearts? 6 ischemic RVs vs. 6 nonischemic RVs
 Does RV gene expression differ by RVEF in endstage LF HF explanted hearts? 4 normal RVEF cases vs. 8 abnormal RVEF cases
Our analysis will involve both IPA and KEGG for molecular systems/pathways, but also for individual genes of interest, ANOVA with Benjamini/Hochberg correction and "q value" is our tentative plan (at least as is planned now). Given the small samples size and "shifting" samples sizes for and possible analyses, want to ensure that before the VICTR application is reviewed, it's reasonably argued and feasible. If you could also suggest a biostatistician with particular expertise in analysis of "smallish" microarray pilots, would appreciate it as well. Using Affymetrics whole transcriptome array. Usually 250500 genes show differential regulation.
 Adequacy of sample size will depend on crosssubject variance of log gene expression among other things
 Pathway analysis is probably necessary, vs. individual gene screening
 RV and LV intrinsically paired; n=15 or 29
 Controls are varied; n=4 probably futile; large false negative rate
 VICTR applications can request specialty biostatistics support (here, genomic analysis) or any amount; More than $2000 (20 hours) must be matched 1:1 with funds from home Division
 Estimate of time required: 30 hours ($3000 voucher request)
Tricia ThorntonWells, Jennifer Vega  MPB, Neurosci
Consultants: Hakmook Kang, Bill Dupont, Frank Harrell, Dan Ayers
 African Americans, majority family hx Alzheimer's disease
 Neuropsych measures of cognition to be correlated with functional connectivity measures (withingroup design)
 Also interested in betweengroup design
 Initial VICTR application used standardized effect size in power calculations
 Is it possible to use an effect size in real units? Or base calculation on precision of parameter estimates?
 Regression framework: Y = cognition X = connectivity measures; need small # connectivities of interest (may limit it to one)
 Can compute sample size needed to yield a small margin of error in estimating any one correlation coefficient (n will be at least 100)
 PS software will compute power for a slope
 Still not clear how to state the final result in biologic or patientmeaningful units
Razmia Alawi, Nursing
Consultant: Frank Harrell
 High turnover of nurses on gen surg floor
 Who to interview (prob. floor managers) and when to interview them
 Think about 2 stages: one qualitative phase to get universe of answers from some of the mangers, one to ask all managers to rate the applicability of these answers to what they've seen
 Suggest talking to Ken Walston, Warren Lambert, Irene Feuer
14 Aug 2012
Elizabeth Pearce, ENT resident, Dept. of Otolaryngology; PI David Francis
Conducting a prospective study for which we need Biostats help both with formatting the statistical design, and for a price quote for Biostats consult for eventual data analysis. it is a prospective study, with testing once before and once after surgery for vocal fold immobility, to measure multiple parameters, the most important of which is respiratory rate during a treadmill test. We spoke with Dr. Christopher Slaughter who recommended repeated measures statistics, which we are unfamiliar with doing on our own. However, we are comfortable doing stats for the other measures in our study.
Study Design: This is a prospective, feasibility study of patients with Unilateral Vocal Fold Immobility (UVFI) being treated with injection laryngoplasty procedure. We hope to have 10 patients complete study, enroll 2025 (need to do power calculation). Patients will undergo testing once preoperatively, then once, 3weeks postoperatively. UVFI can worsen voice, breathing, and swallowing due to an insufficient glottis (voice box). The injection laryngoplasty is a standard of care procedure designed to improve glottic closure. Our primary outcome measure is Respiratory Rate (RR, breaths/minute) during an exercise tolerance test. Patients will serve as their own control, comparing pre and post treatment results.
Aim: Comprehensively evaluate patient respiratory function pre and postinjection medialization.

Our primary outcome measure is change in Respiratory Rate (RR) during the exercise tolerance test (see description below) between pre and posttreatment time points.

RR: In healthy subjects, RR range from 3 breaths per min (br/min) to 28 br/min. The mean rate was
 49 br/min with standard deviation of 4.36 br/min. ( Addison PS, Watson JN, Mestek ML, Mecca RS.An algorithm for pulse oximetry derived respiratory rate (RR(oxi)): a healthy volunteer study. J Clin Monit Comput.
 Feb;26(1):4551. Epub 2012 Jan 10.)

We will also measure other outcomes, listed in Table 2, yet RR is our primary outcome. The other outcomes are dichotomous measures and we are comfortable doing these less complicated stats.
Hypothesis: Pulmonary function, measured as Respiratory Rate during exercise test, will decrease (improve) after injection because it enables patients to efficiently use the Valsalva maneuver to control intrathoracic pressure and breathing.
Exercise Test :
Patients will walk on a treadmill using the Bruce Protocol (Table 1), with incremental increases in speed and incline every three minutes. Data collected: Our primary outcome is to monitor is respiratory rate (breaths/minute). This will be measured once before the test, then at the last 20 seconds of each 3 minute stage (7 stages total if patient completes entire protocol), then lastly, once at the end of the treadmill test for 9 total time points. We will also measure: Heart rate, Blood prssure, METs, Oxygen saturation, total time on the treadmill, and highest Bruce Level achieved. Currently, there is no normative data in the literature about RR for the treadmill test, or patients with UVFI.
Table 1: Bruce Protocol Exercise Treadmill Testing
Stage

Minutes

% grade

km/h

MPH

METS

1

3

10

2.7

1.7

4

2

6

12

4.0

2.5

6.6

3

9

14

5.4

3.4

9.1

4

12

16

6.7

4.2

12.9

5

15

18

8.0

5.0

15.0

6

18

20

8.8

5.5

16.9

7

21

22

9.6

6.0

19.1 
Table 2: List of Tests Performed Once Pre and Once PostTreatment
The table below outlines tests performed within each of the three major parameters: Swallowing, Voice, and Respiratory. (S) = Standard of Care, (R) = Research ( in bold ).
Swallowing Parameters

Voice Parameters

Respiratory Parameters

Dysphagia Handicap Index (DHI) (S)

Voice Handicap Index (VHI) (S)

Pulmonary Function Tests (PFTs) (S)


Sustained Phonation (S)

SF36 Quality of Life Survey (R )


Stroboscopy (S)

Chronic Respiratory Questionnaire (R )



10point Dyspnea Scale (R )



Exercise Treadmill Testing (R )



PostExercise Survey (R ) 
 Pilot study based on convenient sample size (N=15), wanted to calculate power
 Have repeated measures data. Simplify the power calculation based on paired ttest. Can use PS software.
 Need to control the ability (at baseline, how many blocks they can walk), which associates with how far they can go during the study.
 Multivariable model, 1:15 rule (need 15 subjects for each covariates included in the model)
 Will apply VICTR grant, estimate 40 hours of work (~$4000).
24 July 2012
Martin Schmidt, Psychiatry/VKC, Adam Anderson, BME
 Mass spec with mass/charge ratios, by multiple regions or voxels
 Has functional data analysis of protein spectra been extended to this setting?
 Discussed advantages of unified modeling that respects spatial structure
 Wavelets are worth thinking about
10 July 2012
Ryan Delahanty, Epidemiology
Robert Turer, Trent Rosenbloom, DBMI
 ICD9 vs ICD10; important to map between, to e.g. compare on old data
 ICD10 more specific, includes laterality, more procedures
 GEMs: general equivalence mappings; include 1:1, 1:many, combinations
 No prospective research examining codergenerated 9, 10 codes vs. GEMs: validation of GEMs
 May want in the future to pick at random two coders for each chart, to be able to study interobserver disagreement and see if this amount of disagreement is in the same range as coder vs. GEMs
 Current data not allow this
 Choose 100 cases, hopefully diverse
 weighted Kappa
3 July 2012
Genie Hinz,
DBMIEvaluation of four different risk scores. To evaluate the benefits and limitations of the survey version as compared to the electronic version.
 missing information on ADL data from 44 patients.
 Create 2by2 table for survey and electronic version separately using review chart as a gold standard
24 April 12
Adeline Dozois, Brian Cash, Gadini Delisca, Alejandro Perez, Pooyan Rohani, Emily Zern
Research question: For patients presenting to the Accident and Emergency (A&E) Department of Georgetown Public Hospital Corporation (GPHC) with Staphylococcus aureus infections, is there an elevated level of IgG antibodies against two virulence S. aureus virulence factors (lukA/B and αhemolysin) following a fourweek convalescence period relative to the IgG level during acute infection?
Study design: Blood samples will be collected from 150 patients visiting the A & E of GPHC over a fourweek period. These subjects will be asked to return in two to four weeks for a repeat blood draw. Blood samples will be spun at the GPHC clinical chemistry laboratory and divided into two aliquots. One aliquot will be stored at the GPHC, and one will be sent in batches to Vanderbilt University. Paired serum samples will be analyzed by ELISA to determine total IgG concentration, as well as IgA concentration measured against two specific S. aureus virulence factors.
Sample Size : 150
 Guyana main public hospital; 60% of African descent, 40% Indian southeast asian
 Prevalence in staph aureous infections of resistance
 Immunological analysis from blood samples; 4w later additional blood sample
 ELISA IgG nonspec plus 2 specific vir. factors; describe antibody response; are antibodies still there 4w later?
 Planned paired ttest for acute vs. recovery rise
 Want to compare with VU population
 BlandAltman plot: Y=postpre; X=post+pre; desire: random scatter that's flat
 Alternate: log (post/pre) vs. geometric mean ... log(pre) + log(post)
 Or: square root or cube root
 Check for difference being on the right scale, i.e. transformed variables properly
 Decide on transformation from 4 plots; transform > take differences > paired ttest or Wilcoxon signedrank test (latter somewhat preferred)
 Population differences: double difference: compute f(post)f(pre) in two populations; compare using WilcoxonMannWhitney rank sum (unpaired) twosample test
 The latter test is independent of the transformation
 Sample size: need raw data, quartiles, or SD
 Tertiary analyses: regress age, weight, sex, ... on f(post)f(pre) (multiple regression)
 Or: regress age, weight, sex, country ... on f(post)f(pre)
17 April 12
Alexandra May, Trisha Pasricha, Ian McGuinness, Richard Samade, David Amsalem, Zain Gowani
21 Feb 12
Genie Hinz, Biomedical Informatics
 Look at outpatient populations, 4 year mortality rate on a sample around 3000 (identified pts with specific physicians)
 Suggest keep it as continuous as to divide pts into low, intermediate, and high risk
 Used age, gender, etc. to define the pts as low, intermediate, and high risk, if the primary interest is the risk, then no need to adjust for other risk factors
 One of the biggest confounders is activity daily living, with 0/1 reading, while in another study ADL has scores from 07; Why got similar ROC? Might because all other variables already explain the variance,
14 Feb 12
Matthew Duvernay, Pharcology (mentor: Heidi Hamm)
 VICTR application regarding sample size and statistical analysis plan, needs more detail
 Paired samples: PAR1 stimulated vs PAR4 stimulated, compare protein expression
 Need to know how many subjects needed to detect statistical significance
 Outcome: mass spectrometry intensity
 Suggest take log2 transformation and use pairedt test to calculate the required sample size
 Analysis: Use Wilcoxon signed rank test to compare within subject, which does not assume normal assumption
 Get at least 3 samples from pilot data to get an estimate of standard deviation
17Jan12
Special Clinic: PREDICT
 Josh Peterson, Kevin Johnson, Marc Beller, Ioana Danciu, Jennifer Mitchell, DBMI
 Biostatistician discussants: Bill Dupont, Frank Harrell, Cindy Chen, Jonathan Schildcrout
 Understanding phamacogenomic effects
 Preemptive genotyping, starting with clopidogrel for patients getting a stent
 Topic today is evaluation of the program
 What is response of end users when they get the new information?
 There is a variety of both efficacy endpoints and safety endpoints to consider
 Related to clop. metabolism there are 2025% heterozygotes and 2% homoz.
 What simulated decision analyses will help?
 Any simulation will have uncertainty that is limited by the literature's margin of error in estimating differential treatment effect, no matter how many "patients" are simulated
 Cost of genotyping vs. cost of using another drug
 Information base for differential treatment benefit (strong pharmacogenomic hypothesis) is now confusing for CYP2C19
 Challenges of retrospective evaluation (e.g., changes in concomitant therapy) vs. prospective (cat is out of the bag with respect to FDA genotyping recommendations)
 How would one design a good randomized clinical trial? Genotype everyone, mask random half
 parallel group
 matched cohort
 retrospective casecontrol study  genotype upon MACE (major cardiovascular event) plus controls; may have to avoid using VU
 observed estimate of efficacy is a function of:
 relative efficacy and safety of clopidogrel vs. prasugrel
 genotypedifferential benefit of prasugrel
 average baseline risk of major cardiovascular events (if study is prospective)
 how the different endpoints are weighted (esp. bleeding)
 C4PG or Case4PG web site deals with some of these factors
 some of literature only provides (fuzzy) estimate of differential benefit of clopidogrel over placebo
 there is one good paper in the literature for clop. vs. pras. by genotype
 if prospective, completeness of followup is imperative
20Dec11
Raafia Muhammad, Cardiovascular Medicine and Lan Jiang, CHGR
 Went over BioVU application and developed a plan for validating 100 SNPs found in a discovery cohort
 Recommended plotting log odds ratios from discovery cohort vs. log odds ratio from validation cohort
 Need to clarify whether to adjust all these for age, sex, etc.
Rachel Lippert, MPB
 Analysis of race effect on SNPs, looking at low BMI
 SNP has been identified in the 1000 genome database
 Make sure any selection biases in cases and controls are similar by design
 Suggested 11 casecontrol ratio, using low and high BMI
25 Oct 11
Katie Hutchinson, Graduate Student, Cancer Biology
 Study of genetic mutation of cancer. One tumor sample, one normal sample. Want to compare the distribution between two samples.
 Suggest using graphic display of the data of the two samples
 Circos: data visualisation for the genomic data
2 Aug 11
Ben Shoemaker, Maureen Farrell, Cardiology
 Went through Cox model analyses with updated data  significantly longer followup
 May be a good idea to get more patients; current data can be used to plan how many, depending on followup schedule
26 July 11
Ben Shoemaker, Maureen Farrell, Cardiology
 Implantable cardiac defibrillator (ICD) before ventricular tachycardia/fibrillation (VT/VF) (primary prevention)
 Refining criteria for selection of patients for ICD
 Genetic marker
 DISCERN : multicenter trial, VU was a center; SNP 4q22 p=1e8 related to axon development
 Validation cohort from another study; primary prevention subset; completely independent of discovery cohort (no sample contamination)
 Need to extend followup of all patients, especially to handle the problem with one patient having an event at 745 days when others were not followed but 730 days
 Went through Cox model analyses and issues with covariate adjustment
 See if one missing LVEF can be filled in
5 July 11
Yosaf Zeyed, Pulmonary, Allergy, Critical Care Med, Dept. of Med.; MSCI
 Applying for VICTR funds  gene micro RNA in acute lung injury
 Aim 1: 5 groups (ALI w/sepsis; ALI with trauma, sepsis without ALI, trauma without ALI, control  from ICU without any of those)
 6 pts/group
 Outcome has already happened; serum samples pooled because of cost
 If do arrays individually by patient, cost is about $15,000, possibly feasible with VICTR funds
 Up to 1000 candidate features/markers; this is the dimensionality of the problem
 Aim 2: screen for specific micro RNA
 Discussed problems with high false negative rate
 Could screen markers based on uniformity of expression in 6 patients within the group, then look to see if different groups have differently homogeneously expressed markers
 Note that the more funds requested from VICTR the more the mentor has to be involved in the application
28 June 11
Jamie Ausborn, cancer biology
 Four cell lines, three replicates of each, microarray
24 May 11
Sarah E. Williams, Ronina Libeth, Pediatric Clinical Research UURP
 Bill Dupont went through the use of PS software, discussing relationship between power, detectable difference, etc.
 Need an estimate of the standard deviation across patients
3 May 11
Phil Lammers (Department of Medicine, Hematology, Oncology)
8 Feb 11
Satish Raj (Clin Pharm) and Kirsten Haman (Psychiatry)  "Origins of Cognitive Dysfunction in Postural Tachycardia Syndrome (POTS): A Pilot Study"
 Biostatisticians in attendance: Bill Dupont, Pencheng Lu, William Wu, Lily Wang, Theresa Scott
 VICTR submitted protocol that went through pre prereview (Frank was reviewer); asked to attend a Biostat Clinic.
 Did not have list of specifics that needed to be addressed, but discussed general improvements, which included:
 Modify plans for "matching":
 Instead of "gross matching on intelligence", exclude those subjects/patients who score below a population level intelligence from the analysis.
 So, will only be matching on age.
 Dr. Dupont mentioned using "frequency" matching instead of individual matching.
 Providing more detail of where control subjectswill be recruited from.
 NOTE: protocol refers to cases as "patients" and controls as "subjects".
 Provide more clinical reasoning for using the zscores in the analysis  common analysis? how zscore calculated? how reproducible?
 Provide more clinical reasoning for using the "2SD" cutoff  is this cutoff calculated from the sample or is it more from a population/clinical standpoint.
 Possibly using regression analysis to analyze those with normal or abnormal (based on 2SD cutoff above) composite score between cases and controls  instead of comparing using just a ChiSquare.
 Adjusting case/control effect for continuous composite score.
4 Jan 11
Ken Monahan, Cardiovascular Medicine: Sleep Apnea and Atrial Fibrillation
 VICTR grant nearing end
 Need sample size for larger study
 Genetic contribution to propensity for Afib given sleep apnea
 Using BioVU; 1500 with blood banked, EKG, sleep study
 Limitations of sample: many who didn't get sleep test have sleep apnea; many who get sleep test will have SA (enriched sample)
 Think about target population and how can you approximate the estimates that would be obtained from the target pop.
 Model for probability of (binary) Afib  genetic + clinical factors
 Model with clinical factors; add sleep apnea factors; add genetic factors
 Limiting aspects of sample size: number of Afibs in the 1500; allele frequencies
 Can take a prediction approach
 Candidate predictors: 10 clinical/demographic, 5 sleep study, 30 SNPs > 15*(10+5+30) Afibs = 675
 15:1 rule of 15 events per candidate regression coefficient comes from simulation studies of how many outcome events are needed per candidate variable in order to fit a model that is as good as it apparently is
 This is assuming that all SNPs are given equal prior importance
 If SNPs are restricted to have 10 effective d.f., would need 15*(10+5+10) = 375 Afibs
 To answer a question about genotype x apnea interaction would probably require many more subjects
 Alternative: something like group lasso where one finds the optimum crossvalidating shrinkage of the 3 types of variables (3 shrinkage coefficients)
 SNPs shrinkage factor of infinity > genetic information can be ignored
 Crude estimate of biostat cost $8000
14 Dec 10
Matt Landman
VICTR proposal  mutational analysis of hereditary pancreatitis pedigrees
23 Nov 10
Yan Ru
VICTR proposal 1288
genetic profiles using Affymetrix cDNA arrays, using normal, MMR, IMR groups
5 samples for each grp, id differentially expressed genes, will verify with PCR
about 28000 genes will be examined
suggest:
 when comparing groups, need to account for within group dispersion. Can use T statistics rather than fold changes, which only account for changes in means
 need to evaluate false discovery rate. 10% cut off.
 need to increase sample size to at least 10 to 15 per group. (Original proposal is 5 per group for three treatment groups.)
 One strategy would be to use only two groups, normal vs. MMR, and increase the number in each group.
 factors to consider: will be publishing for this pilot data? feasibility assessment needed? effect sizes expected to detect?
2Nov10
David
PCA analysis  the first principal component score is the linear combination that explains largest amount of variation in gene expressions among all linear combinations so
Amanda
Imaging problem
 need to do a linear regression with intensity as outcome, group as independent variable, and date as covariate variable
Dale Tylor
 retrospective cohort study  VICTR voucher request is being made
26Oct10
Andrew Link, working with Kathy Edwards, Pediatrics Infectious Disease
 Reponse to vaccination: novel biomarkers
 Goal is to have a test for whether the vaccination protects a subject
 Without waiting for 7 days, for example if antibodies are tested
 Proof of concept by comparing vaccinated and nonvaccinated subjects
 Query immune system after vaccination
 Tcells, memory cells expensive to measure
 Blood sample 1, 3, 7d after vaccination; analyze neutrophils, Bcells, Tcells, ...
 Profiled for changes in transcriptome (RNASeq) and proteome (mass spec)
 Cytokine responses
 Naiive patients used in first round, to try to deal with alreadyprotected subjects
 Could require prevaccination titer to be below a threshold
 May be beneficial to study a vaccine that is 50% effective  wouldn't need controls
 One way to get a handle on the multiplicity/lack of validation issues is to generate a random matrix of the same dimension as the real data, and to do all "real" analyses in parallel on the random data; if one obtains an ROC area of 0.85 on the real data and 0.55 on the fake data, then we have more confidence in the results. However if the ROC area achieved with the fake data is also 0.85 we have to worry.
5Oct10
Chantel Sloan, Division of Allergy, Pulmonary, Critical Care, Dept. of Medicine
 Air pollution data over time, spatial modeling, spatial interpolation, modeling different land uses
 Consider data reduction methods such as missing data PCA
 Can have Chris Fonnesbeck (ecological statistician) attend a future clinic
 Be sure to factor in uncertainty in alphas for all later steps
Adrienne Dula, Radiology
 Came to last Thursday clinic
 New imaging technique; interested in sensitivity and specificity
 Gold standard is as used in clinic
 Goal: distinguish tissue types, e.g. white vs. gray matter
 For a career development grant
 Current plan  overlay segmented image on new method
 Can you register using histology?
 Can one use ratios of contrasts?
 Discrepencies between edges, dealing with one method not finding an edge
 Might use rank measures; not detected = worst rank
Xue Yang and Bennett Landman, Electrical Engineering & Computer Science
 Related question; how is one image related to another
 Comparison of two random variables; Y, random Xs, fixed Zs (e.g., demographics); Y  X, Z
 Nonparametric voxel by voxel regression but worried about random Xs
 Likelihood P(Y, X  Z) = P(Y  X, Z) P(X  Z)
 Could fit a time series model followed by a group analysis both using SPM
 May start with (Y  X)  Z; if multiple images (not just two), can analyze all possible pairs (or k1 pairs for k images) and use the cluster bootstrap to get confidence intervals taking into account what information overlaps
14Sep10
Sarika Peters, Dept. of Pediatrics and VKC (VICTR prereview clinic)
 Consultants: Frank Harrell, Warren Lambert, Lily Wang, Cindy Chen
 Preliminary data  imaging study  children with Angelman's syndrome  14 patients, 13 controls  ages 817
 To be used for a Feb11 R01 submission
 To use same imaging techniques, eye tracking, event potentials
 Need to demonstrate feasibility of conducting study at VU with a study team in place
 Target 10 patients to get 6 (2 + 2 + 2); issue of need for sedation during imaging (50 min.); seeing about one pt/wk
 3 syndromes  different genetics, similar phenotypes, but involve different brain areas
 Goal is feasibility, not group differences
 Did analyses using 2 independent imaging interpreters, estimated interrater reliability
 Brain regions of interest predefined
 Difficult to deal with medications patients are on (e.g., antiseizure)
 In ultimate analyses might think about correlating key variables of interest with time since last dose; can also look at which medications were used
 For R01 imaging will be at baseline only
 Asking VICTR to pay for scanning, sedation, ERP, work towards power/sample size for R01 based on first preliminary data (N=14 + 6 patients)
 VICTR proposal doesn't need to ask for much biostat time
 Resources: VICTR, VKC Statistics and Methodology Core , Dept. of Pediatrics biostat resource (senior statistician: Ben Saville)
Ileko Mugalla VIGH question from 13Sep10 clinic
 Who are resources in qualitative research? Sabina Gesille in Gen Peds, prople working with Len Bickman in Peabody; Warren can be contacted to help fine more collaborators
31Aug10
 Ehab Kasasbeh, Cardiovascular Medicine  see here http://biostat.mc.vanderbilt.edu/wiki/Main/BasicSciClinicAnalyses#Ehab_Kasasbeh_Cardiovascular_Med(consultant: Bill Dupont)
 Demonstrated PS software for computing power to detect specific odds ratios in the twosample binomial problem
 Borden Lacy, Stacey Seeback, Microbiology and Immunology (consultant: Fei Ye)
 Question about displaying standard deviations
 Potential problem with analyzing on the percent or ratio scale
 May be able to compute confidence intervals on the log scale, antilog to get foldchange confidence intervals
 Be sure to display raw data
 Issues of within and betweenplate normalizations
 Not a good idea to divide by mock treatment  display groups, not one group "normalized" for the other
 Need a full model that will take into account the variability in the mock treatment group
 In the first phase of the SI genome project want to pick genes from 21,000 genes
 Depending on biologic and technical variability, there may be a significant problem with false negatives
 Pilot study being used to refine technique, reduce variability
 Bennett Landman and Baxter Rogers, Radiology and VUIIS (consultants: Frank Harrell, Bill Dupont; Lei Xu is out of town)
 Neuroimaging studies are very expensive
 EMR can be used for retrospective analysis
 Does a treatment outcome correlate with something that could have been discovered by imaging
 E.g. subregion size, shape, amount of fluid surrounding the brain
 Also interested in creating atlases showing normal biologic variability; anatomical and resting state; PET as static capture of function
 It may be possible to contribute images to BioVU
 Images come with a set of parameters describing the data acquisition/hardware
 Lossless compressed image data will be maintained as a separate database linkable with the SD
 VICTR Design Studio suggestions
 Babar Parvez, Cardiovascular Medicine (consultants: Bill Dupont, Pengcheng Lu)
 399 atrial fib patients who were rhythmcontrolled
 Literature identified 3 genes (4 SNPs) related to increases risk of afib (through ion channels etc.)
 Do these have to do with altering the effect of meds (rate vs. rhythm control meds, etc.)
 Polymorphisms thought to mainly effect rhythm control
 Have the results for the 4 SNPs
 Y = success/unsucessful rhythm control
 Traditional to combine various genotypes; but what to assume for heterozygous?
 Twoparameter logistic model will allow the entire spectrum from recessive to dominant
 Could also do an allelebased analysis (related to minor allele frequency); a 2x2 table analysis based on alleles is equivalent to a personbased binary logistic model that places heterozygous exactly halfway between aa and AA
 Spoke about potentially increasing the study's power by turning the binary response variable into a continuous or ordinal variable
17Aug10
Jongchan Kim, Dept. of Pathology
 Known loss of function mutations
 # copies > 2 vs. 2; # copies not available in data (which are from the literature)
 Would have been far more powerful had we had # copies or level of protein expression
 Need to know when metastasis was determined (baseline vs. followup)
 Survival time from date of diagnosis (all cause death); need to confirm is in days (but how did fractions of a day come about?)
 Need time to last known alive if not died, plus death/censoring indicator
 PI:Sarki Abdulkadir  have him come to a future clinic
Queen HenryOkafor  Cardiovascular Medicine
 Biomarkers on acute decompensated heart failure on patients presenting to ED
 gout or goutrelated arthritis patients excluded (would have been on the drug)
 Uric acid of interest; does lowering the level reduce the need for revisiting ED
 E.g. 7 mg/dl to 6.5 mg/dl in 3 months by putting on drug
 Starting sample: all qualifying patients who had a baseline and a 3 month visit
 Check association of uric acid change with later ED visit events
 Two ways to state the hypothesis:
 Does the medication help (would need some patients randomized to not get the medication)
 Whatever the medication does, does the resulting change in uric acid explaining a reduction in future events
 Can't say that the drug did it (as opposed to a temporal effect/natural history of HF)
 Doing the second study could lead to funding to do the second
 Can the level of HF be measured at the initial and 3m visits? Can correlate 2 uric acids and 2 HF measures (e.g., change vs. change)
 For sample size estimation for a randomized trial (drug vs. placebo, Y=HF symptoms e.g. ED revisits, other HF assessments)
 Need to refine the outcome scale: how many levels, estimate fraction of subjects in each level
 The more continuous the HF outcome scale the lower the sample size
 If only use the time until the first ED visit after 3m, need to estimate the incidence of ED visits over time
22Jun10
Jon Forbes, Neurosurgery
 Electrical activity of slices of rat brains; stroke model; stop oxygen perfusate
 After signals stop, restart perfusate, electrical signals back in 6m. Elect. stimulation at reperfusion > faster return
 2 groups: stopped, wait for signal cessation, observe for spontaneous recovery; stopped, signals lost, restore perfusate, estim; also record time to signal loss
 Y: Time to recovery of signal; no animal failed to have signal return (because of perfusate)
 n=30 rats (15 each group)
 Box plots with raw data are a good idea; decided on just dots
tco.lost < data$tco_lost rec < with(data, c(spon_re, stim_re)) group < c(rep('Spontaneous', 15), rep('Stimulated',15)) xless(data.frame(group, rec)) require(lattice) dotplot(group ~ rec) Ecdf(~ rec, groups=group) bwplot(group ~ rec)
pdf('/tmp/p.pdf', width=5, height=3.5) stripplot(group ~ rec, jitter.data=TRUE, factor=.5, xlab='Time to Recovery of Baseline Electrical Activity (min.)', panel=function(...) { panel.bwplot(...); panel.stripplot(...) }) dev.off()
25May10
Erik Boczko, DBMI
 Measuring information content
 Two fluid samples from same patient; each sample used for same series of correlated tests
 Goal is to see if a noninvasive test is accurate. The genetic analysis can rank order concentrations better than estimate absolute values
 Testing 7 pathogens, and a universal bacteria test. Culture takes too long; Coulter counter cannot differentiate staff aureous etc.
 Need to show equivalence between noninvasive and invasive assessments
 Interesting to look at invasive test ordering tendencies; if invasive test ordering is deterministic, study could still be useful for differential diagnosis of organisms; otherwise there may be a problem with invasive test nonorders.
 His major concern at this point is assessing false negative results from his noninvasive test. His approach appears to be reasonable. As he is somewhat concerned about confidentiality, it is probably not appropriate to provide more details here.
18May10
Amanda Solis, Dept. of Microbiology & Immunology
 Confocal imaging of HIV particles in cell culture media; immunofluorescent staining; intensity reading on red = antibody
 How well is antibody binding to certain types of virus
 Comparisons of interest: For one type of antibody is there a difference in binding between two viruses, and for another type of antibody is there no difference in binding? But interested in answering separate question for each antibody.
 3000 intensity readings per sample; geometrically random but can't say that antibodies attacking one virus are not the same antibodies counted for another virus
 Starting from the same virus culture have repeated the entire experiment 34 times
 How do two samples differ in intensity readings? Comparison of virus types.
 Could pool all data but distinguish the replicates by having a 14 "replicate variable; allows checking for replicate effort
 Recommend having the instrument output intensities in a 10x10 grid; argue that there is very little overlap in particles between adjacent squares, so effective sample size is 100 (times 34)
 Test for difference in viruses:
 ttest if take sample size to be 34
 WilcoxonMannWhitney twosample ranksum test (short name: Wilcoxon test) if use 10x10 grid (Wilcoxon test can't give small Pvalues unless n is a bit larger than 7 in both groups combined)
 No binning of data is needed
 Could use a regression model to test whether there are any geometry effects (e.g., add as a variable to the model the distance from the center, distance from the closest edge, etc.)
 Gain more information about distributions by plotting histograms or cumulative distribution functions of intensities over individual viruses
2Mar10
Discussion of Biomarker Discovery and Validation Strategies
 Should the validation measure be the same statistic as the one used to find the markers and adjust for covariates?
 Example: LR chisquared for finding "winning" markers, use likelihood measure for measuring predictive discrimination
 Binary logistic regression: logarithmic scoring rule (equiv. to 2 log likelihood minus a constant from a model with no covariates)
 Note: quadratic scoring rule is Brier score
 General case: Nagelkerke R^2 or "Adequacy index" (measure of relative information content)
 Development of predictive model uses doseresponse relationships
 Validation should likewise use doseresponse relationships
 Example: develop risk score S using Cox model; using linear combinations
 Relate S to survival in a validation sample: need to show that mid S values correspond to patient prognoses in the midrange
 Dichotomization of S at a point c implicitly assumes a discontinuous relationship that is flat on either side
 More importantly it assumes that c is a true inflection point or otherwise is a risk threshold; if c is a sample quantile or mean, it is derived without reference to risk at all
 If S was derived from a continuous function need to validate it continuously and also show that middle values correspond to middle outcomes
 Calibration (reliability) curves are also of interest
 In many cases, it is of interest to partition S into S(clinical) + S(biomarkers) and show the variation in outcome if you vary S(biomarkers) and fix S(clinical)
 Continuous approach removes temptation to find alternative cut points c if initial validation is disappointing
 Can also consider measures (e.g., slope shrinkage) of how different a recalibrated curve is from the original calibration curve from the training sample * There are measures of pure discrimination ability (Cindex = ROC area; discrimination index related to R^2)
 Simple measures can also be used, e.g., histogram of predicted risk, scatterplot of predicted risk based on clinical variables alone vs. predicted risk based on clinical variables + biomarkers
15Dec09
Buddy Creech, Pediatrics Infectious Diseases
 Hemoglobin variation and staph aureous/disease severity
 Clinical outcome is invasive vs. noninvasive (in bone) disease
 Interested in applying for BioVU usage
 1214 hemoglobin residues of interest
 Plan on a discovery phase and a validation phase
 Would get more power with a continuous or ordinal outcome variable
24Nov09 1Dec09 15Dec09
Merida Grant, Psychology, Arts & Sciences
 Applied for VICTR funds; was asked for clarification re: data analysis plan
 Discriminative conditioning in unipolar depression
 3 groups: depressed with and without hx of childhood trauma, age+gendermatched controls
 No fMRI data collected yet
 Frequently extract beta weights from Brain Voyager and feed them into SPSS
 3 main brain regions of interest; want whole brain (voxelwise) analysis and regionwise (should use one of these 2 for sample size analysis)
 Need to specify the difference desired to be detected; difficult on unitless fMRI data
 fMRI data: subtract baseline from active task
 Need to do BlandAltmanlike plots to verify that the data have been properly transformed before taking differences, i.e., differences are independent of averages
 Make sure that any normalization method used has tested the assumption that division is the correction normalizing operation and that log ratio should not be used instead
 Find literature to get estimates of standard deviations; will look for similarities (stimuli, imaging technology)
 May simplify to a twogroup problem
 May have to assume that SD in the control group is the same as in the other groups
 If it makes sense to log values, or otherwise use a relative (e.g., fold change) measure of effect, then the power calculations are simpler
 This assumes the literature provides SDs on the appropriate scale (e.g., log of raw values)
 Otherwise can approximate an absolute mean difference to detect by multiplying the mean by for example 0.2 (20% change)
 Watch out for variance in clinical samples tending to be much larger than variances in controls
 1Dec09: Lei has a copy of a pertinent article comparing two groups (placebo, corticosteroid); use discriminative conditioning
 Compared contrast CS+  CS across the two groups
 Paper makes the common mistake of showing a dynamite plot with error bars not corresponding to the experimental design
 Need SD of withinsubject CS+  CS differences across subjects
 Otherwise need to estimate the correlation coefficient for CS+ vs. CS
 But also provided the maximum t statistic over the 32 voxels within a cluster; tstat could be large just because the SD was underestimated in a voxel
 Better to use region of interest average as the basis for designing the new study, but this is not available from this paper
 Simple approach: solve for SD from the maximum t and use Bonferroni multiplicity adjustment when computing power; hope that Bonferroni is conservative enough to account for the downward bias in SD; i.e. use alpha=.05/32 = 0.0015625
 Need the one voxel mean difference one would not want to miss
 t = 3.01 = [(1.4  .2)  (.2  .4)] / [sigma * sqrt(1/20 + 1/28)] = 6.148 / sigma; sigma = 2.043
 NOTE: This may pertain to ROI
 What is required is the biological difference one would not want to miss, on the same scale as the 2.043
 For example if the delta to detect is 0.5, and alpha=0.05 power=.8, n per group must be 263
 Used PS software available from PowerSampleSize 15Dec09
17Nov09
Tracy McGregor, Pediatric Genetics
 Planning sample size for validation SNP study in scoliosis
 17 SNPs survived discovery phase
 Power vs. precision (of odds ratios) approach; casecontrol study
1Sep09
See Hardesty 18Aug09
George Jules, MMC
 Question about microarray analysis and GO
25Aug09
George Jules(Heart), Supervisor: Darryl Hood
 Look at the raw data by logging in VMSR Vanderbilt, think about changing platform to Affy
 Discuss experimental design, suggest increasing sample size from 3 mice to 6 in each group(control, treatment), minimizing the system errors
 The experiment will be done in one month
 Will talk with supervisor to discuss the collaboration with our department
18Aug09
William Hardesty, Chemistry; return 1Sep09
 R save() data frame is attached
 Suggest using various of lasso/elastic net with Cox model, forcing age and sex (don't penalize them) into the model
 62 patients with melanoma
 Comparing protein expression to survival time
 Original approach was to pick a winner by examining separate associations
 Simultaneous modeling with penalization has many advantages; take observed associations with a grain of salt; discounting
 Went through usage of coxpath; best model by AIC or BIC had dozens of features but coefficients may be small
 May be worth looking at coxme function for quadratic penalty
21Jul09
Charles Flynn, Surgery
 Protein and lipidomics pre and post bariatric surgery
 lean, obese, NASH with 10 patients each
 ChangYu had estimated power was high; however power estimate was based on the assumption that a single marker to compare (across the 3 groups) was prespecified
 Perhaps 3000 candidate lipids to narrow down to one or a few "significant" marks that distinguish the 3 groups
 May be important to get technical replicates because of variation caused by ions being laid down while the current ions are being processed; but costs $1000/sample
 Lipids extracted by biopsy; rudimentary matching of spectra and proprietary principal components analysis
 Have applied for VICTR funding to supplement a funded grant
 Two DDRC projects funded and pooled
 Aims are quantitative lipidomics and imaging lipidomics; interest in nonalcoholic fatty liver disease
 a pattern of lipid deposition can promote a specific liver disease
Consultants:
LilyWang,
NateMercaldo,
FrankHarrell
30Jun09
Hernan Correa (Pathology); Maria Piazuelo (Medicine); Pelayo Correa (Medicine, Program Project Grant)
 GI cancer varies greatly from mountain to coastal areas of Columbia
 Results to h. pylori infection modulated by a GI parasitic disease
 Mass spectrometry MALDITOF; 90 subjects; lowrisk (coast) and highrisk (mountain) groups
 Subjects are males 4059 years old with symptoms requiring endoscopy
 Surface epithelium is of interest; variation of protein expression over the different histologic types will also be of interest
 Need file with m/z information and a file with clinical information (age, histology,, ...)
 Cancer Center Biostatistics may be able to help if this provides preliminary data for a grant proposal (VICTR can provide support for prelinary results too; not for executing an existing grant)
 This may lead to a line of research in children related to eosinophilic esophagitis (this may qualify for VICTR as a new entity; VICTR application will have to make it extremely clear how this is not primarily related to executing a funded grant)
 Suggest applying for a $8000 voucher from VICTR and will need to explain how this fits with the alreadyawarded voucher
 Target is Dec09
 Consultants: Pengcheng Lu, Yu Shyr, Ming Li, William Wu, Steven Chen, Frank Harrell
7Apr09
Ralph Passarella, Undergrad, Molecular and Cellular Biology; working in Radiation Biology
 Submitting a paper on mice tumor response to therapy after a peptide treatment
 Using imaging to get % binding, comparing treated to untreated
 In analyzing response to treatment need to separate binding from vasculature response
 6 treatment groups; each has n=4 except positive control (n=9) and one technical problem resulting in an n=3. These pertain to plates (cell culture dish). Cell lines were used. Plates represent experimental units.
 Need to display raw data; see DynamitePlots
 Might display as a 3x2 matrix of dot plots or group pairs
 Unified analysis with 6 treatment groups, organized as a twoway ANOVA with a 3x2 setup, is recommended
 Assumes normality and constant variance across 6 groups; assumes data properly transformed
 Alternative approach: rankbased analysis; proportional odds ordinal logistic regression can test for interaction and test contrasts
 Proportional odds model is a generalization of the Wilcoxon/KruskalWallis test and does not require one to properly transform the response variable (% binding); it is also robust to outliers and does not assume normality of the raw data
 Data layout (email to biostatclinic@list.vanderbilt.edu): column for celltype, column for treatment, column with response measurement; # rows = total # plates (long and thin spreadsheet)
 Suggested R code
library(Design) dd < datadist(data); options(datadist='dd') f < lrm(response ~ tx * celltype) contrast(f, list(tx=, celltype=), list(tx=, celltype=))
18Dec07
Dan Kaiser  Medical Student 4th Yr
p < spss.get('/tmp/Data_All.sav', lowernames=TRUE) sort(names(p)) table(p$drug.fre) s < subset(p, drug.fre==1) s < upData(s, pr=ifelse(pr==888, NA, pr)) describe(s$pr) describe(s$aceid) spearman2(aceid ~ pr, data=s) with(s, rcorr.cens(pr, aceid)) with(s, rcorr.cens(pr, as.integer(aceid))) z < .27422/.114525 z 2*pnorm(z) library(lattice) bwplot(aceid ~ pr, panel=panel.bpplot, data=s) bwplot(aceid ~ pr, panel=panel.bpplot, data=s, datadensity=TRUE) range(s$pr) bwplot(aceid ~ pr, panel=panel.bpplot, data=s, datadensity=TRUE, xlim=c(100,250)) Ecdf(~pr, groups=aceid, data=s)v < varclus(~ qrs + qt + rr + qtc + pr + age.enro + height + weight + lvmass, d ata=w) plot(v) v < varclus(~ qrs + qt + rr + qtc + pr + age.enro + height + weight + lvmass + as.integer(aceid), data=w) plot(v) < transcan(~ qrs + rr + qtc + pr + age.enro + height + weight + lvmass + as.integer(aceid), data=w, iter.max=200) summary(v)
13 May 2008
Discussions: Amino acids, pseudocounts and "propensities"
17 Nov 2008
Discussions: M. ChambersETD mass spectra charge determination modeling Suggestions: logistic regression (unordered or ordered?); multinomial logistic regression; get a probability for each category? select N most probable charge states instead of just top probability? make sure normalization is not predictive of outcome (charge)
20 Jan 2009
Discussions: Jiajun Shi: Gene Association studies; twostage confirmation; Winner's Curse:
http://www3.interscience.wiley.com/journal/121591995/abstract"Metaanalysis" of data collected for a different study: can data collected for a control/breast cancer/diabetes study be reused for studying a different phenotype (obesity)? Some paper has already done this, but faculty survey says that's a bad idea because of the variation it would introduce.
24 Feb 2009
Discussion: Britney Grayson's Sample size and power analysis for copy number variation chip. Experiment Design: 10 diseases (Type I diabetes) 10 controls, they are 10 monozygotic twins; >1.8 million spots Suggests: Adapt design by adding samples based on initial results, power calculation for the qPCR outcomes. Come in next week
3 Mar 2009
Cathy Derow: binary outcome
install.packages('glmnet') library(glmnet) library(Hmisc) xless(glmnet) y < read.csv('/tmp/relapse_vector.txt',header=FALSE)$V1 table(y) w < read.table('/tmp/wang.mxp',sep='\t') x < t(as.matrix(w)) xless(x) f < glmnet(x, y, family='binomial') plot(f) f coef(f) xless(coef(f)) cof < coef(f) apply(cof, 2, function(x)sum(x!0))
Note: Had to run R as the superuser, otherwise R could not find =glmnet
10 Mar 2009
Issue about limits of knowledge of multiple markers related to a binary endpoint, and futility of finding a cutpoint.
library(Design) x1 < runif(50, 2, 2) p < plogis(x1) y < ifelse(runif(50) <= p, 1, 0) dd < datadist(x1); options(datadist='dd') f < lrm(y ~ pol(x1,2)) table(y) plot(f, x1=NA, fun=plogis)