Health services research, diagnosis, and prognosis

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Current Notes (2021)

2021 October 25

Kevin Liu (Mohana Karlekar), Hospital medicine

Retrospective cohort analysis comparing integrated versus traditional palliative care consult model for clinical outcomes of length of stay, code status change, and looking for risk factors associated with code status change and code status resistance in the first 12 months of the COVID-19 pandemic. VICTR Biostatistics voucher. Mentor confirmed.

2021 October 18

Hannah Smith (Don Arnold), Pediatric Emergency Medicine

Title: INCIDENCE OF SUICIDAL BEHAVIORS AND ASSOCIATIONS OF PATIENT CHARACTERISTICS IN CHILDREN BEFORE AND DURING THE SARS COV-2 PANDEMIC Introduction: Suicide is the second leading cause of death among children and young adults aged 10-24 in the United States. During the Covid-19 pandemic, there has been a noted increase nationally in pediatric ED (PED) chief complaints related to suicidal ideation (SI) or suicide attempts (SA). Identifying characteristics of these patients will inform resource-allocation and interventions. Our objective was to identify relevant characteristics of pediatric patients presenting to a tertiary, urban children’s hospital ED with concern for SI or SA. Methods: We reviewed the electronic medical records (EMR) of patients requiring psychiatric consultation in our PED between March 2018 and March 2021. Patient data was included for analyses if there was documented concern for SI or SA. We recorded age, gender, race, zip code, state, insurance, mode of arrival, urine drug screen results, Columbia-Suicide Severity Rating Scale (CSSR) scores, ED arrival date and time, ED length of stay, and disposition. For patients admitted to the hospital we recorded length of stay, avoidable hospital days and barriers to discharge. We specifically would like help in studying correlative data between trends seen in patients presenting for psychiatric evaluation as well as trends seen in UDS results and CSSR scores. VICTR Biostatistics voucher. Mentor confirmed.

Clinic Notes:
  • Interested in number of mental health (suicidal ideation or attempt) cases within the emergency department before and during pandemic. March 2018 versus march 2020/2021. Screened relevant variables from March 2018-Feb 2021. Included patients for study if they had presenting complaint of SI/SA or were found to have SI/SA on chart review. Variables include demographics, Columbia-Suicide Severity Rating Scale, los. N is ~4100 records. Patients who presented for multiple times have multiple records.
  • COVID could have affected the number of patients in either direction. Pandemic could increase the mental health pressure but patients may tend to avoid hospital visit because of the pandemic. This could be a limitation of the study since it's hard to distinguish the two forces that affect the number of patients. Looking at associations of clinical outcomes with patient characteristics. Need to specify research question(s). Could describe the data, describe he data in a time series, or do inference.
  • Possible VICTR voucher project for biostatistics support (90 hours). Application website (https://starbrite.app.vumc.org/ ) and research proposal template ( https://starbrite.app.vumc.org/funding/templatesforms/ ).

2021 October 04

Majd El-Harasis (Dan Byrne), Cardiology

Evaluation of complications after Watchman in patients with cerebral amyloid. Question re: matching and statistical analysis in the setting of low outcome numbers. Mentor confirmed.

Clinic Notes:
  • Looking in to differences of complications after procedure, rare procedure/outcomes. Did Fisher's exact test due to small sample size.
  • Should this be descriptive in nature because of sample size? Any model you build needs to be constrained regarding outcomes and sample size of data set. Better off from model stability, power perspective to model underlying probability number instead of 3-level classification (in general, continuous variables provide more power). If continuous number is not available, best to report descriptives. Matching helps build more complex model but with this data will still probably not be able to do much. But could do matching and analyze the data as secondary/exploratory analysis.
  • Generally speaking need 10-15 outcomes for each parameter.

2021 September 13

Jayla Thompson (Laura Hall), Pharmacy

My project is looking into the association between different racial groups and pegaspargase-related toxicities (I.e. pancreatitis, hepatotoxicity, hypertriglyceridemia). I already have a draft of data points to collect, and I want to make sure I’m not missing any that would be important to this study. I also want to discuss what statistical analyses would be appropriate for this study. Mentor(s) confirmed.

Clinic Notes:
  • Looking for guidance on statistical methods. EHR, retrospective cohort study. Nov 2017-Nov 2021. All pediatric patients being treated for specific type of leukemia. Outcome is toxicity of pegaspargase. Only patients treated at Vanderbilt for leukemia are included. Race group is self-reported (be careful as EHR sometimes is not). Primary question: Do specific ethnic groups have different toxicity? Toxicity is collected via lab values, typically on weekly basis. Different grades of toxicity. Dose adjustment rarely happens. Patients need to have a baseline lab value (obtained before first drug administration) to be included. The expected cohort is about 100-130 patients. The racial breakdown of the cohort is similar to that of the VUMC patients. Some challenges of the study include estimates of some racial groups may not be precise due to the number of patients. Also the follow up periods of patients would be different.
  • Recommendations:
    • Think about using severity/degree of toxicity instead of dichotomous (y/n), tend to have more statistical power this way
    • If looking at differentiating between length of time before toxicity, use time to event analysis. You will have time varying covariates, modeling response profile over time.
    • Descriptive analysis to show difference in racial groups. Report average number of toxicities divide by total follow up time. Could also report on the level of toxicities by patients. This does not take in to consideration any adjustments and is a very crude analysis.
    • VICTR voucher for biostatistics support is an option if needed.

Montana Fleenor (Jennifer Reed, Will Tidwell), Pharmaceutical Sciences

This is a retrospective pre/post study to investigate whether phenobarbital monotherapy is non-inferior to a symptom triggered benzodiazepine regimen for management of alcohol withdrawal in trauma patients. Patient charts will be reviewed to assess for incidence of complicated alcohol withdrawal in each group. Questions to address include: noninferiority vs. superiority, use of delirium free days vs. delirium days. VICTR Biostatistics voucher. Mentor confirmed.

Clinic Notes:
  • Retrospective cohort study, compare new phenobarbital protocol to benzodiazepine regimen for management of patients with alcohol withdrawal. Is there improvement in new protocol, superiority study. Most of the data will be pulled using ICD-10 codes. Challenge with all outcomes is that they might not be captured after discharge. Could restrict outcomes that only occur during inpatient setting. Estimated sample size of 500-1000 patients. Anticipated incidence rate is 10-15% in the benzodiazepine group, 0-7% in the phenobarbital group.
  • Recommendations:
    • Should we use delirium free days or delirium days? Delirium days (free days does not handle death).
    • Possible VICTR voucher project for biostatistics support (90 hours). Need to see possibility of funding additional resources. Part we are concerned about is cost-effectiveness; that is not in the expertise we have in VICTR team. Application website (https://starbrite.app.vumc.org/) and research proposal template (https://starbrite.app.vumc.org/funding/templatesforms/).

2021 August 30

Aysu Erdemir (Robin Jones), Hearing and Speech

I request consultation for the stats on my study. To date, we have used a linear mixed effects modeling approach (using lmer from R) to look into between group differences (persistent, recovered and nonstuttering children) for speech rhythm measures across different utterance durations. We have currently created three chunks of utterance duration (short, medium, long) to be used as a fixed factor, but we are also interested in including duration as a continuous fixed factor - however we would like to be able to access the possibility of a non-linear association in the data (between duration and our dependent variables for our three groups). We are seeking input on assessing how to set up such a model in R (if appropriate). As a note we also include all repeated utterances from each participant into the model rather than using average scores. We included random slopes for repeated utterances to account for within-subject correlation of utterances collected on a particular child. Therefore, each participant contributes a different number of utterances to the statistical model. Mentor confirmed.

Clinic Notes:
  • Speech rhythm measures, 3 groups (stuttering recovered, stuttering persistent, non-stuttering), 50 kids total (10 persistent, 20 recovery, 20 non-stuttering), Particularly interested in the comparison between recovered and persistent groups. All talk for 4 minutes, devise utterance numbers from recording. Duration of utterance highly impacts speech rhythm measures. In general, all patient characteristics are equal across groups. Want to look at join relation of duration & joint supra.
  • If looking for repeated measures type analysis, challenge is that number of measurements you have is related to the performance of the patient.
  • There could be multiple chunks within a 4-minute period. And there could be short, moderate, or long utterances in chunks.
  • Recommendations:
    • For future predictions, think about whether you want summaries of utterances or entire 4-minute recording.
    • Look at heat map of duration (x) by sbpr (y), estimates relationship between duration/sbpr as predictors of group (persistent vs recovered).
    • To account for non-linear duration, use polynomial or restricted cubic spline.
    • Could use a model comparison to determine if a polynomial term/restricted cubic spline term is significant.
    • To do a patient-level model, the number of utterances should be the same for all rows for a single patient.
      • Create summary measure of number of utterances and control for it in the model.
    • Think about prospective analysis predicting group.

2021 August 16

Nicholas Pietrini (Joshua Beckman), Department of Internal Medicine

Cost-effectiveness analysis of using an ankle-branchial index to screen for peripheral artery disease. Looking for biostatistical assistance and VICTR grant to assist with executing Markov model microsimulation. All variables obtained and model designed. For our specific case, we are modeling the use of an ankle-branchial index to predict peripheral artery disease in the general population and the possible improvements with mortality, morbidity, and cost to see if a population-based screening methodology is cost-effective. This link provides a brief description (see the Markov Model section) https://www.ncbi.nlm.nih.gov/books/NBK374339/. Mentor confirmed. VICTR biostatistics voucher.

Clinic Notes:
  • Want to create model knowing inputs to screen for peripheral artery disease - is it cost-effective. Test is binary based on threshold (ankle-brachial index). Screening ABI is done rarely. The cost of the test is consistent. One time test, follow for rest of life to see if can tolerate medicine (statin, ace inhibitor). Using US life tables 2017 for state transition probabilities. Assume general population utilities.
  • Need to nail down key questions you want answered at the end of the day. In addition to cost-effectiveness, might explore what populations you see different rates of screening or different levels of implementation, or what point is it cost-effective vs not (what are drivers of this and what makes it flip), what is robustness of conclusions based on assumptions, how does compliance impact this. Think about potential interactions. Evaluate assumptions that the model start with.
  • Possible VICTR voucher project for biostatistics support (90 hours). Biostatistics team to see possibility of funding additional resources. Part we are concerned about is cost-effectiveness; that is not in the expertise we have in VICTR team currently. Application website (https://starbrite.app.vumc.org/) and research proposal template (https://starbrite.app.vumc.org/funding/templatesforms/).

2021 August 02

Ryan Millager (Robin Jones), Hearing & Speech Sciences

Our project involves ordinal regression among stuttering variables in a large extant data set. We are looking for support in interpreting orm output from rms R package, specifically re: model fit, odds ratio, and 95% CI. Mentor confirmed.

Clinic Notes:
  • Series of pairwise ordinal regression models. Population is children with stutter (~270 records).
  • Standardized coefficient for ordinal regression model:
    • First step - scale outcome measures/ variables to have units of standard deviation. Then coefficients are log relative hazards (translate to odds ratio to report). To interpret - standard odds ratio (change in odds for 1 unit change in predictor values); here it is change in odds for per [1 unit] standard deviation change in predictor.
    • You can also have a unit IQR as standardization (interpret - if I change covariate from 25 to 75th percentile what type of change is expected in outcome). For standard deviation, scale function centered and scaled the values. For IQR, center it at 25th percentile instead, scale it by dividing by difference of 75th - 25th percentile (use quantile function to find IQR).
  • Another way to look at relations/effects of predictors on outcome is partial effects plot. plot(Predict(orm.model)).
  • P-Values: be sure to understand we either see detectable difference or it is not conclusive. If we get additional data maybe we would see a difference.
  • Model fit to help choose most efficient model and remove "useless" variables:
    • First approach - LASSO (package glmnet), doing model building and variable selection on same data set. Looks at variables simultaneously, creates an importance measure to give ordering of variables. More established techinique.
    • Second approach - random forrest importance factor
    • If you intend to perform inference, you have to be super careful about the methods you use for variable selection. Most do not work with inference with same data set. We think clinical expertise is best way to choose variables.

2021 July 26

Stephen Wilson, Hearing & Speech Sciences

Questions about using R to analyze a longitudinal stroke recovery dataset. A particular challenge is that recovery trajectories are correlated with initial severity. Dataset arising from an R01 on recovery from aphasia (language deficits) after stroke. I have 100+ subjects with scores at 4 timepoints reflecting speech/language function at 5 days, 1 month, 3 months, and 12 months. I would like to model the trajectories of recovery. It is tricky because there are many missing data points, and the extent of recovery clearly depends on the initial severity, so I need to model that instead of just trying to model a global recovery curve. My initial thought was to fit a mixed model with random intercepts and random slopes for patients, but it's tricky because of the missing data points and the shape of the recovery curve being unknown.

Clinic Notes:
  • This is a longitudinal study of stroke patients. 360 patients with 4 timepoints. Have 120 with 2+ timepoints. Goal is to describe typical trajectory for recovery. There are missing values. Missing values that are bookended could be imputed. Those that are not bookended have problems with imputation. Some patients are unable to be tested due to severity but have scores at a later time.
  • Random intercepts can be problematic in these situations. One option is to model transitions from one timepoint to the next. Robust standard error may be useful in these models.
  • https://cran.r-project.org/web/packages/robustlmm/vignettes/rlmer.pdf
  • Jonathan Schildcrout could be a good resource.

2021 June 28

Michael McCann, Medicine/General Internal Medicine/Hospital Medicine

Two projects with similar means of analysis. The first project is very simple in its statistical analysis and we are in the development stage with enrollment about to take place (which will likely occur at a slow pace over years).

  1. Hospital Medicine Resident Elective: we are designing an elective for medical residents to improve in their ability to proficiently care for hospitalized patients supported by use of a self-rated competency-based assessment. Surveys are created and being revised. We would use Pre/Post testing with scaled surveys.
  2. We have developed a feasibility study involving the design six ninety-minute sessions to occur every other month. We want to measure patient compliance and patient engagement. As this project moves forward, it hopes to improve educational exposure and training on team-based decision-making in taking care of this population. We hope to see an improvement in the skill set of interprofessional team-based care of the inter professional students. We will monitor number of patients invited to the sessions, number of patients who arrived, level of involvement of individuals, and proportions of individuals who return for future sessions. We will also track whether individuals have made contacts with each-other and their level of communication.

To measure the educational aims of the study with regards to interprofessional team-based collaboration and education, we will be using validated tools such as the Team Skills Scale (TSS), which is a 17-item tool with a 5-point scale that is used to assess interprofessional team skills, in conjunction with the Attitudes Toward Health Care Teams Scale (ATHCT), which is a validated 20-item tool measures team members’ perceptions of the teamwork required to deliver care. There are other interprofessional validated scales to consider using such as the Interprofessional Collaboration Scale and the Team Fitness Test to measure team dynamics and communication. For improvement in culturally competent care, we apply to use the Inventory for Assessing the Process of Cultural Competence Among Healthcare Professionals – Student Version (IAPCC-SV) to measure improvement in skills.

Additionally, through the qualitative analysis, we hope to see that creating an environment in which interprofessional learners take on value-added roles in the care of VFF patients, there is an overt recognition in the need for interprofessional collaboration in accomplishing improved patient-centered outcomes. Based on the qualitative data gathered by the VPIL students, valued information can be discovered with regards to concrete problems affecting patients’ health care as well as aligned solutions. The sessions will be recorded. Each session will have a briefing and debriefing of the students. The VFF team can gather valued data regarding patient centered outcomes via qualitative analysis of not only the sessions, but also the briefing and debriefing of the sessions. Some aspects of care that are hoped to be gained are insights to patient and learner perspectives on barriers to care and utilization of community resources. The qualitative analysis will focus on learner centered outcomes as well as patient centered outcomes. VICTR voucher request.

Clinic Notes:
  • VFF Service, works with high utilizers, developing system for tracking patient-center outomes, creating support group (Team based care) and collecting data by using validated performance surveys. Pilot study to see if feasible to have people show up (biggest barrier). Outcomes in professional students, some outcomes in patients. Goal 1) group of patients who share their own information, second goal 2) what is barrier to decrease utilization from provider/team perspective.
  • Recommendations
    • First question is qualitative. Use moderated focus group approach (seek help from qualitative core - contact Chris Lindsell for names).
    • Second part, collect data from group with trained professionals, then have interprofessinal students conduct them after they observe. Worried about lack of training from students. If you are using same questionairre for pre and post, if there is a recall bias it could be emphasized by that.

Paras Karmacharya (Leslie Crofford), Rheumatology

Follow up of sample size and prediction modeling from meeting on 6/21. How to calculate ICC and report it for visits vs. unique patients? How to account for treatment change within 1 year (assuming these would be patients that did not respond at all)?

Clinic Notes from June 21: Defining psoriatic arthritis phenotypes and treatment response.
  • Q1. Power calculation for prediction model-verify what I did. Outcome is remission, based on an ordinal score (PsAID). Rather than dichotomizing into a yes/no, could model the ordinal outcome over time. Three models are planned: using baseline only to predict response, to predict trajectory, and using both to predict response. The data for this study are already collected, so we can assess power of the proposed analysis, rather than a true sample size calculation. There are multiple option to assess model performance. Tom: reccomend thinking about calibration. Goal is to covey that the complexity of the model can be supported by the data that are available.
  • Q2. How can we adjust for treatment changes in trajectory analysis (growth mixture model)?

Clinic Notes:
  • Developing predictive models for treatment response in a type of arthritis.
  • Recommendations:
    • Binary outcome is least powerful. Preference is continuous outcome.
    • Aim 2 - explain this is an exploratory aim since you do not know the archetype/number of profiles of the response; will be generating hypothesis from it. Exploratory aim generates hypothesis (ex. based on prelim data analysis, we think there are 4 response archetypes), confirmatory aim has a precise hypotheses you already established and you designed a study to answer that question.

2021 June 21

Paras Karmacharya (Leslie Crofford), Rheumatology

Defining psoriatic arthritis phenotypes and treatment response.
  • Q1. Power calculation for prediction model-verify what I did. Outcome is remission, based on an ordinal score (PsAID). Rather than dichotomizing into a yes/no, could model the ordinal outcome over time. Three models are planned: using baseline only to predict response, to predict trajectory, and using both to predict response. The data for this study are already collected, so we can assess power of the proposed analysis, rather than a true sample size calculation. There are multiple option to assess model performance. Tom: reccomend thinking about calibration. Goal is to covey that the complexity of the model can be supported by the data that are available.
  • Q2. How can we adjust for treatment changes in trajectory analysis (growth mixture model)?

2021 May 17

Amany Alshibli (Bantayehu Sileshi), Anesthesiology

We have conducted a retrospective analysis of perioperative data collected in REDCap at two hospitals in Ethiopia to understand the effect of the COVID-19 pandemic on surgical care and outcomes. We have submitted a manuscript with these results and received revisions from the statistical reviewer. I would like to go over questions I had about adjusting reported outcomes for confounders using propensity score methods and about using a segmented regression to model outcomes over time (i.e. before, during and after COVID lockdown). Mentor confirmed.

Clinic Notes:
  • Looking at impact of pandemic on surgical care and outcomes in Ethiopia. Retrospective analysis. 3 exposure groups based on time 0) pre-covid 1) "lockdown", no elective 2) after "lockdown", no elective lifted. Want to understand case volume, referral patterns, outcomes (28-day cumulative mortality) in the 3 different exposure groups. Primary outcome is 28-day mortality. Secondary outcome is change in surgical case volume in phase 1, 2 compared to phase 0; change in referral pattern.
  • Reviewer feedback: Adjust all outcomes and associations for confounders, Sample size/power justification, Type 1 error and adjustment for multiple comparisons, Before-after design need to use segmented regression, plot over time.
  • Recommendations
    • Plots - For case volume, suggest to create a plot (profile/longitudinal) with cases on the Y, time on the X. Estimate what case volume is in phase 1 with confidence interval. For categorical variables, plot is the same but now Y is proportion of the category over time. Possibly stacked bar chart.
    • Could use risk scores if they will apply in a low resource setting. Present analysis as we are interested in descriptives and degree of change/association.
    • Type I error rate - Could suggest to reviewer that we are not in a setting that would be concerned with family wise error rate and multiple comparisons. Could convert to estimation with 95% CI, rather than formal hypothesis testings.
    • Segmented regression - Helpful in situations where you have administrative thresholds, policy changes. Allowing a jump in outcome at a specific time point. If you do segmented, would report plot.
    • Power/sample size - Recommend to not do this. They are basically asking for post-hoc power. Explain, sample size was determined by all available data. Information about effect sizes and precision is contained within the confidence intervals provided, not a power analysis. Large research suggests this should not be done (provide references).

2021 May 10

Ryan Millager (Tiffany Woynaroski), Hearing & Speech Sciences

My mentors/collaborators and I are planning a systematic meta-science project to evaluate diversity of participants in published research, and we wanted to discuss our analytic plan. Mentor confirmed.

Clinic Notes:
  • There has not been comprehensive look at misrepresentation of minorities in human research. Will look at 4 journals, 5 time points across 10 years. ~2000 articles. Coding system to capture information about participants, study design, study region, participant race, ethnicity, sex, gender, etc. This study is setting up idea of reporting and recruiting diverse samples.
  • What are reporting practices for race, ethnicity, sex, and/or for research particpants? Are participants representative of broader regional and national population demographics? Will compare to US census population.
    • Better off reframing as what magnitude of difference exists (estimation): To what degree are minority racial groups under-represented? Absolute percentage point difference.
    • Possible problem with null hypothesis, studies do not represent racial distribution of people across the US. Might be helpful to do random sample of publications weighted by size/number of participants in the studies, do deep dive to identify what the catchment area is for particular studies and its racial breakdowns in that area.
    • Statistical methods discussed: Graphically (radar plot, bar chart); will have confidence interval from percentage point difference to dictate "significance".

2021 April 26

Dupree Hatch, Pediatrics/Neonatology

This specific question is part of a larger project looking at using scavenged data from mechanical ventilators to predict the ability for preterm infants with respiratory failure to be safely extubated (removed from mechanical ventilation). As part of this project, we would like to validate the accuracy of the scavenged ventilator data as this method for measuring ventilation parameters has not been described before. To do this, we are comparing the ventilator data to 1) direct observation by a research assistant, 2) medical records review by trained clinicians, and 3) downloads from Epic. My specific question I would like support with is: What is the most rigorous way to compare the ventilator variables to the methods of obtaining ventilator settings mentioned above? We have binary, nominal, and continuous variables of interest that we would like to compare across the methods. We have considered Bland-Altman, percent agreement, intraclass correlation, etc. but would like an opinion from the Clinic before proceeding further.

Clinic Notes:
  • NICU ventilator patients. 3 NICUs. 2 yrs of data. We want to validate data coming out of mechanical ventilators to validate data & method of collection. Comparing ventilator data, direct observation (gold standard), EHR data, EPIC data. 7 continuous variables, 1 nominal variable. Have done percent agreement/concordance, correlations. A lot of repeated measures on same patient, every hour. Time is how we match up data but want to compare data values to get at comparison of collection methods. Assume time stamps are accurate.
  • How best to analyze the data?
    • Analyze continuous measures with Bland-Altman, pairwise with gold standard. Report limits of agreement between alternative and gold standard. Have correlated/clustered data here that Bland-Altman won't account for. To account for the correlation structure, look at Bland-Altman by machine or within each NICU, if comparable then you can combine them for second step analysis. If systematic differences, combine in more of a meta-analysis type of way (would want more sofisticated analysis).
  • Possible VICTR voucher project for biostatistics support (90 hours)

2021 April 12

Raymond Zhou (Dolly Ann Padovani-Claudio), Vanderbilt Eye Institute

Previous clinic sessions: 11/16/20, 8/17/20, 7/23/20, 4/16/20

We have developed 6 cohorts of patients with varying degrees of Diabetic Retinopathy: No DR (controls), Any DR, Non-proliferative DR (NPDR), Proliferative DR (PDR), Diabetic Macular Edema (DME), and DR without DME, using Vanderbilt’s synthetic derivative (SD). PDR and DME are more severe stages of DR, in comparison to NPDR, which is the first stage of disease. We wanted to study and present the positive predictive value of ICD codes in predicting actual presence of No DR, Any DR, NPDR, PDR, DME, and DR without DME. This is part of a larger project aimed at correlating known genetic variants with Diabetic Retinopathy susceptibility and progression (see previous clinic sessions on 11/16/20, 8/17/20, 7/23/20, 4/16/20. First, records with ICD codes corresponding to No DR, Any DR, NPDR, PDR, DME, and DR without DME. Then, 980 of these records were manually reviewed to confirm the presence or absence (true positives/negatives) No DR, Any DR, NPDR, PDR, DME, and DR without DME. Patients that received codes consistent with PDR may have been determined to have only NPDR, and vice versa. We wanted to report on and quantify the PPVs as they will be relevant to our future analyses and valuable to the scientific community at large. Questions we have are as follows: In many records, we were unable to determine the true clinical history regarding presence and progression of DR. For these cases, we excluded them from our larger study, where presence and progression of DR is our primary outcome. However, if we would like to calculate the PPVs of ICD codes as part of the informatics question, how should we deal with these records? Instead of having a binary positive and negative predictive values, is it acceptable to have a third, undetermined category? All patients that progress to PDR and/or DME develop NPDR first. Thus, for our purposes, true presence of NPDR equates to true presence of any DR. We executed preliminary calculations of PPVs of any DR, PDR, and DME. The PPV of any DR was 95% and NPV of any DR was 89%, with a false positive rate of 5% and false negative rate of 11% (currently excluding the undetermined records). This false positive rate and false negative rate adds another challenge when calculating the PPV of PDR and/or DME. For instance, a record that receives an ICD code for PDR and is ultimately determined to be a control would be a false positive for any DR and PDR codes. Given that we more specifically address these false positives in our calculating PPV of any DR, can these cases be removed from our calculations of PPV/false positives for PDR? Mentor confirmed.

Clinic Notes:
  • Do need to include the uncertain in the calculation of PPV. Report how algorithm/ICD9-10 performs vs manual chart review using confusion matrix by reporting count. Confusion matrix contains calculation information for ppv, npv, specificity, sensitivity. Second step, calculate the best case and worst case PPV. Give it a range. Be transparent regarding definitions for manuscript to detail exactly what you did and how things were calculated.

2021 March 29

Allison McCoy, Biomedical Informatics

We are evaluating scores from an Epic model to predict rapid patient deterioration. We have completed assessment of the model overall but would like to evaluate the model separately for patients outside of the ICU, accounting for patients who have scores outside of the ICU but then get transferred to the ICU.

Clinic Notes:
  • How well does score (0-100) predict deterioration, score is calculated in EPIC every 15 minutes. Involves demographics, vital signs, nursing assessments, lab results. Retrospective, grab worst score in past 36 hours. What is optical evaluation? How can we evaluate the model outside the ICU? At present, this score is not live to providers.
  • Recommendations:
    • Discrimination and calibration. Think about prospective approach. Randomize time point at which you evaluate patients with this score and look at next 24 hours for events. How well does DI score discriminate future events, how does it change if you change time window (might be calibrated for a specific time point only). Create ordinal scale of all outcomes by severity. Possible measures: Tau-A, Tau-C, Goodman-Kruskal Gamma, Ordinal C statistic.
    • If you want to use all data instead of 1 time point could consider random effects model. Correlation through random effect of patient. Model correlation structure.
    • More complicated option: Time dependent cox model, allow score to be covariate that changes over time.

2021 March 22

Romney Humphries, Pathology, Microbiology, Immunology (PMI)

Evaluation of a diagnostic test for Candida resistance - pilot study. Question on value of assessing clinical outcomes observationally.

Clinic Notes:
  • Retrospective cohort. Reoccurance is second event within 6 months of first. Can have multiple follow up events. Antifungal resistance in yeast. Looking at patients who have vaginal candidiasis, recurrent infections. Typically not due to resistance to medication but due to other infection characteristics; however, it has shown some resistance. We want to see how much resistance we see under different testing conditions. Resistance measured at beginning of study (first sample). Resistance is minimum inhibitory concentration, quantitative.
  • Q: Have patient characteristics, is there any value in comparing isolates that showed resistance vs those that did not show resistance? Does resistance predict risk of reoccurrence or not? Also have different testing conditions (pH 7 vs pH 4).
    • Use quantitative measure as predictor of number of follow-up events. Looking at correlation of that value with number of follow-up events.
  • Q: Sample size to answer this question - yes/no?
    • Depends on effect size. Might just have to say, these are the samples we can get and because of this, this is our power & amount of difference we can detect.
  • VICTR - does not require same level of rigor for sample size as these are pilot data studies.

Ryan Millager (Robin Jones), Hearing & Speech Sciences

Previous clinic sessions: 02/01/2021

We would like to discuss our modified research questions and analysis plan for cluster analysis and correlations within stuttering symptomology. Mentor confirmed.

Clinic Notes:
  • Retrospective, cross-sectional study. ~290 children who stutter. We think that some indices of stuttering might cluster together to yield subtypes of stuttering. Want to clarify research question as we received feedback from last session that subtypes could be misleading term to apply to cluster analysis results. Moved away from subtype terminology, characterized cluster analysis as exploratory, primary research questions (RQ) refined.
  • RQ1: to what degree are the difference indices of overt stuttering behaviors correlated?
    • Do not recommend Pearson correlation due to assumption of linear relationship. Spearman-rho correlation matrix would be better as it captures non-linear relationship between outcomes. Pairwise examination - create ordinal regression model for all pairs and include covariates in way it allows non-linear effect (include polynomial or in R a restricted cubic spline). Report overall impact of one outcome predicting the other. This will be basis for matrix you create. It will have all measures of association. 6x6 matrix (30 models). Would not worry about p-values, use coefficient and confidence interval.
  • RQ2: Do stuttering behaviors predict or correlate with cognitive-affective manifestations?
    • Interactions are hard to detect, need large sample size to detect them. Need to determine if interactions are worth it. Ordinal regression model (kiddycat/tocs2 as outcome, all 6 stuttering variables/3 adjustment variables as covariates). Possible to combine variables from Q1 if variables are extremely correlated. This is answering do all these variables as a whole help predict kiddycat or tocs2.

2021 March 08

CANCELLED: Allison McCoy, Biomedical Informatics

We are evaluating scores from an Epic model to predict rapid patient deterioration. We have completed assessment of the model overall but would like to evaluate the model separately for patients outside of the ICU, accounting for patients who have scores outside of the ICU but then get transferred to the ICU.

Megan van der Horst (David Wright), Chemistry

The TB biomarker LAM is present in urine at low levels. However, we are finding that the mannose capping motif on the LAM molecule is not detectable in urine samples. We would like to determine the number of clinical samples to demonstrate this negative result (that the mannose caps are either not detectable or removed from the molecule). We have clinical samples available (50 TB+ and 40 TB-). Mentor confirmed.

Clinic Notes:
  • Have samples from patients with TB with ManLAM bacteria. Want to establish the prevalence of cap among those with backbone. Not sure how to handle false negatives. The concentration varies for a sample (concentration is lower if HIV- vs. HIV+). Greater ability to detect in HIV+ patients, could detect about 60% overall (Detect ~60% of HIV-, unknown what proportion of HIV+ but expected to be more than 60%) . Cap could possible not be detected because concentration is low--they want to conclude not detected because it is really not there (not just because concentration is low).
  • Q: How many clinical samples to say these caps cannot be detected in the samples?
    • Find amount of tolerance you are willing to accept (comes down to clinical community, what is small enough value to get your clinical community to agree that is it close enough to 0). Then reverse engineer number of samples to get an estimate of the prevalence under assumption that it is 0. Calculate expected confidence interval of prevalence, increase sample size to the point that CI is completely within your threshold.
    • FH suggests using the 3/n rule. If one observes no events out of n trials the upper 0.95 confidence limit for the probability of the event is very close to 3/n. To have confidence that the unknown probability < 0.01 one would need to observe 0 events out of 300 trials.

2021 February 22

Ivana Thompson, OB/GYN

This project examines the birth volumes (SVD, C/S), surgical abortion and surgical miscarriage volumes at VUMC from 2011-2019. We have created graphs to visualize the volume trends. I would like to apply for a VICTR biostats voucher to collaborate with a biostatistician to examine/analyze for any relationships between the slope of delivery volume curves and the abortion/miscarriage curves. VICTR biostatistics voucher.

Clinic Notes:
  • Q: Any way to compare slopes/rate of change for different pregnancy outcomes over time? A: Options - 1) counts 2) relative proportion. What is the overarching question? Can we predict abortion and surgical pregnacy loss volume from delivery volume? Possible time series data (empirical/count trends don't tell you about the future as well). May consider adding 2020 data (question may come up).
  • Q: Surgical early pregnancy loss/abortions by trimester - is there any way to compare the slope before and after a specific time point. Hired a fellow trained faculty in 2016. A: Impact of specific event, in general, best to think about events and code them in the data before you look at any trend (ie. running measure that describes characteristics). Correlate running measures with running trends.
  • Non-parametric smoothers in analyzing raw data.
  • If you calculate proportions, assess how flat proportions are over time. If it is flat, then proportions are a good way to simplify the data down to. If proportions are constant, would be able to just "pull" proportion forward in time - not really a need for prediction then.
  • Time series: for predictions, short term time trend - seasonality, keep it in quarters. For each type of procedure have both long and short term trend. Short term starts over each year. Model both as function of time. Use that to give estimates and project in to future with confidence bands. Have to assume quarters act independently.
  • Are these lines impacted because of the population of pregnant women in Davidson county has changed? Try to get total numbers. If efforts are not effective then increase is due to population increase.
  • VICTR vouchers tend to contribute to general knowledge, not specifically at Vanderbilt.

Kelsey Gastineau (Shari Barkin), Pediatric Hospital Medicine

We will be performing an implementation study evaluating the impact of a training platform on provider firearm injury prevention counseling. We will be surveying providers pre-training, post-training and 1-month following training. We have 2 primary statistical questions: what is the change in reported self-efficacy and what is the change in counseling frequency. Would like to address sample size estimation for both of these questions. Mentor confirmed.

Clinic Notes:
  • Firearm injury prevention, T32 proposal, very beginning of proposal. Developed a multimedia training platform (SAFER) the help counseling tips and tools for pediatric providers nationwide. Evaluate effectiveness-implementation hybrid type 3 design to evaluate the platform. Survey to collect self-reported data, pre/post/1-month post. 5-point likert scale. Expect ~20% change for both questions.
  • Aim 1 - How effective is the SAFER training platform at improving pediatric provider firearm injury prevention counseling self-efficacy?
  • Aim 2 - One month after completing the training, to what extent are providers continuing to include firearm injury prevention counseling during routine annual pediatric exams?
  • Q: Sample size needs? alpha 0.5, power 0.8, effect size 0.20
  • Recommendations:
    • Have delayed start for intervention (almost like stepped-wedge design)
    • Create slider scale (0-100 continuous instead of Likert) for answers regrading self-efficacy (slider in RedCap) - have to touch the slider, make required field to force answer.
    • Check in to pediatric statisticians, or possible VICTR voucher for help with analyses.

2021 February 15

Lina Sulieman, Biomedical Informatics

Reviewing the statistical plan for a projected submitted to VICTR. VICTR biostatistics voucher.

Clinic Notes:

  • Review of stat plan for VICTR voucher. Study is regarding disparities cataract surgery.
  • Q: How much detail is needed in the stat proposal? A: Enough detail so that we can understand, definitions of variables, source of data. Specify statistical model, covariates. This blog may be useful: https://hbiostat.org/post/addvalue

Dakota Vaughan (Sean Donahue), Ophthalmology

Previous clinic session 10/19/2020

Addressing variables for a predictive model for amblyopia given preschool vision screen results.

Clinic Notes:
  • Predictive for vision problems in preschoolers. Outcome is binary, results of vision screen. Hope to report Odds Ratios. Suggest model data, probability of spacial measure. Could use restricted cubic splines, or linear splines.

  • Sample code:

require(rms); dd <- datadist(mydata); options(datadist='dd'); f <- lrm(y ~ lsp(left, 0) + lsp(right, 0) + age, data=mydata); ggplot(Predict(f)) x1 <- pmax(left, right); x2 <- pmin(left, right); f <- lrm(y ~ lsp(x1, 0) + lsp(x2, 0) + age, data=mydata) aniso <- abs(left - right) (could add this to model)
  • Summary will be the picture.

2021 February 08

Mariah Caballero (Yolanda McDonald), Human and Organizational Development

I’ve been working on a project with Sandia National Laboratories which applies text analysis methods to community newspaper data. Our aims are to understand the publication rates of contaminants in the news and their relationship with local economic/social/political variables. For example, I might ask—how does a county’s employment in industrial economies influence the number of water-related articles published on inorganic chemicals? Mentor confirmed.

Clinic Notes:
  • Want to understand water quality based on news articles. Use text analysis, break down to analyze large patterns. Most use people who read articles for content analysis. This is more big data approach. Aims: 1) What social, physical, and political factors influences whether or not an article was published ina county receiving news distribution about an SDWA rule from 2009 to 2018 2) Among countries receiving water quality-related news, what social, physical, and political factors influences the annual frequesny of published articles from 2009 to 2018. Data cleaning is complete, looking for recommendations for analysis. Currently looking at multiple regressions, 1 for each of the rules. Outcome: county distribution (count or binary) by year.
  • Benefit of count data vs. binary - binary only if there is no difference between having 1 vs 6 publications. Only difference is no vs yes, not how many yes. Use proportional odds model for count data. Think about correlation structure of rows of data, do you need to take in to account clusters/random effects? Also keep in mind temporal issues related to newspaper distribution over time (volume of articles published).
  • For later: look in to shared models; however, it is best to start the way you have done it as shared models are more complicated.

Santiago Angaramo (Dolly Ann Padovani-Claudio), Ophthalmology and Visual Sciences

Previous clinic sessions: 01/04/2021, 12/14/2020, 10/12/2020

The impact of HTN on DME development.

Clinic Notes:
  • Temporal impact of hypertension on diabetic macular edema
  • Timing? Last known eye exam for those who did not develop DME is correct censoring time, in time to event analysis. Censoring time for those who developed DME is date of DME dx. There are not really 2 groups.
  • Should we exclude those who do not have interval between date of DR and DME (ie. those not in our health system)? Would make it cleaner to apply medical home definition.
  • Recommendations:
    • Possible look in to time-varying covariate model - take first date of DR as starting point for everyone, time goes forward and see what happens. Variable number of records per patient (BP measurements), diagnosis of DME as outcome. Time: How many days it was since DR, interact BP with how many days it has been since DR. Would need to consider making sure patients have a window of time between DR and DME dx (ex. 6 months). Can have historical BP that has constant effect. Non linear decay - weight of historical blood pressures decreases as time goes on.

2021 February 01

Ryan Millager (Robin Jones), Hearing & Speech Sciences

In our research with preschoolers who stutter, we are investigating the possibility of “subtypes” of stuttering and planning to submit a range of stuttering variables to a cluster analysis. Data has been collected (large extant database), and I am refining my theoretical approach and methods for this first graduate project. I have limited experience with cluster analysis and mostly hope to learn more about this. Primary questions are: what method of cluster analysis would be best for this research? Do we need to make considerations for power (ideal n, ideal number of variables, etc.)? We would also be appreciative of any recommended resources or next steps as we look to practice cluster analysis using R. Mentor confirmed.

Clinic Notes:
  • Stuttering is characterized by frequency or standardized measurement to get severity. It is more complex than that. Our question - is there a way to look at subtypes of stuttering? Are there certain characteristics that tend to stick together? If we do get subtypes, do they correlate with contributing factors driving the profile? ~140 patients.
  • Two-step vs. hierarchical cluster analysis? What this does is solve for centers of clusters, some clusters can be bigger than others. What matters is how close the individual is to the center of the cluster. People misuse cluster analysis due to individuals who are equidistant from 2 cluster means. Non-Heiractical cluster analysis may be preferred to avoid making a determination on heirachy.
  • Are we looking at dimesions or subtypes? Is this a continium? Do we have an achor that measures impact of the characteristics? Something to scale against, the most "important" thing. What this could be is not clear for this topic, reasonable people may disagree. Stuttering presentations are diverse, team is trying to shift from outcome to categorizing patients to gain undstanding.

Inga Saknite (Eric Tkaczyk), Dermatology

Previous Clinic Session: 01/18/2021

We have re-analyzed our data by accelerated failure model and tested nonlinear 4-knot, nonlinear 3-knot and linear models, based on Dr. Harrell’s suggestions at the Biostatistics Clinic on 21/01/18. We would appreciate Dr. Harrell helping us interpret the new results. Is it reasonable to pick the best model based on AIC only, if AICs among the 3 models are very similar? AIC values: Nonlinear 4-knot: 149.0, Nonlinear 3-knot: 150.3, Linear: 149.3. Nonlinear 4-knot slightly better than linear model AIC – should we choose this one? LR chi squared: Nonlinear 4-knot: total 6.55 (p=0.0878), nonlinear 2.90 (p=0.2344), Nonlinear 3-knot: total 6.72 (p=0.0349), nonlinear 1.11 (p=0.2914); Linear: total 4.1 (p=0.043). Survival risk is routinely assessed before hematopoietic cell transplantation (pre-HCT) by currently the best available predictor, Disease Risk Index (DRI). Robust post-HCT biomarkers to predict outcomes, especially relapse, are lacking. We propose a novel, noninvasive imaging biomarker that can be assessed post-HCT, based on real-time, direct visualization of adherent and rolling leukocytes (A&R) in cutaneous microvessels by confocal videomicroscopy. In a pilot study (N=56), patients with high A&R counts shortly after transplant subsequently had significantly increased rates of relapse and decreased relapse-free survival (RFS), compared to patients with low A&R counts. Mentor confirmed.

Clinic Notes:
  • Return visit to review analysis updates. This is a biomarker study to predict treatment failure relapse or death).
  • Suggest using log normal model.
  • Frank: concern about investigator created degrees of freedom. Also suggest age to be included in model.

2021 January 25

Tavia Gonzalez Pena (Jessica Young), Obstetrics & Gynecology

We are planning to describe patterns in legal outcomes among a retrospective cohort of pregnant and postpartum women with opioid use disorder participating in an integrated program offering prenatal care and addiction treatment. We would like to review: 1) our descriptive primary analysis plan (specifically what would be the best test for a count without categories) 2) our logistic regression plan; and 3) what to do with missingness for our covariate data. Mentor confirmed. VICTR biostatistics request.

Clinic Notes:
  • Wanting to describe legal outcomes for pregnant/post-partum women with opiod use disorder who have particpated in perinatal recovery program (VMARP). Exclude those who experience pregancy loss. Include those who delivered between 01/2017 to 12/2020. Data extracted from medical record.
  • Questions:
    • 1) our descriptive primary analysis plan (specifically what would be the best test for a count without categories). Looking to do descriptive straitified by outcome of interest (dcs referral, criminal penalties, housing/employment)
      • First, will want to quantify what proportion 1) should have been referred and were, 2) should not have been referred and were not, 3) should have been referred and were not, 4) should not have been referred and were. Wilcoxon is for comparing two groups on contiunous or ordinal outcome.
    • 2) our logistic regression plan
      • Possible to concentrate power by ordering list of outcomes by severity. Look at peers/literature to see if that type of outcome is "usual". If they are all similar in severity, counting seems fine. If not, ordinal is better. Would be best to have clinical consesus from a group about severity/ordering of each outcome. Proportional odds model for ordinal outcome will handle count, binary, continuous, ordinal data and will allow you to adjust for items. Ordinal scale, the more categories the better.
    • 3) what to do with missingness for our covariate data
      • Administrative, Informative censoring. First thing is honest reporting, descriptive statistics that quantify how big holes in data are. What type of person has missing data alot? How does it vary with race, etc? Treat missingness as an outcome to predict it to try to learn patterns of missingness. Proportional odds model can be used to predict missingness as well as binary logistic model.
  • Recommend applying for VICTR Award for biostatistics support (90 hours). Application website (https://starbrite.app.vumc.org/) and research proposal template (https://starbrite.app.vumc.org/funding/templatesforms/). Please contact Tom Stewart with questions.

2021 January 18

Laura Baker (Eric Tkaczyk), Dermatology

Previous clinic sessions: 06/01/2020, 04/27/2020, 01/06/2020

We are returning to the biostatistic clinic and my mentor Dr. Tkaczyk will be present. We have been using the chronic GVHD consortium to study whether the extent of body surface area (BSA) involved by erythema at study visit 1 and visit 2 correlate with survival outcomes in patients with chronic GVHD. At a previous biostatistic clinic, Prof Harrell suggested using likelihood ratio to evaluate the “added value” of the BSA parameters to the existing parameter of NIH skin score. We have computed the likelihood ratios and would like to make sure that our interpretation of the “added value” is correct. Additionally, we have used spaghetti plots to graph the patients’ skin involvement over time. We would like to learn more about using B-splines to demonstrate the overall trends of spaghetti plots. Mentor confirmed.

Clinic Notes:
  • Chronic graft-versus-host disease is number one cause of late treatment related death after stem cell transplant. Aims: 1. track temporal course of erythema and sclerosis 2. extent of skin involvement and survival (visit 1 & 2 erythema body surface area, added prognostic value). data from 9 centers, 2 populations (incident - enrolled within 3 months of diagnosis, prevalent - enrolled 3+ months since diagnosis). Visits every 6 months. ~185 patients.
  • How to display data and summarize change over time for erythema?
    • Recommend incorporating patient level information (random effect of individual)
    • Desire to use cox model is desire to do time to event. When you have competing risk it is very hard to interpret cox model. Look in to state transition models.

Inga Saknite (Eric Tkaczyk), Dermatology

Survival risk is routinely assessed before hematopoietic cell transplantation (pre-HCT) by currently the best available predictor, Disease Risk Index (DRI). Robust post-HCT biomarkers to predict outcomes, especially relapse, are lacking. We propose a novel, noninvasive imaging biomarker that can be assessed post-HCT, based on real-time, direct visualization of adherent and rolling leukocytes (A&R) in cutaneous microvessels by confocal videomicroscopy. In a pilot study (N=56), patients with high A&R counts shortly after transplant subsequently had significantly increased rates of relapse and decreased relapse-free survival (RFS), compared to patients with low A&R counts. Question: We are seeking advice on measuring prognostic information content of new vs standard predictors via likelihood ratio test comparisons. Mentor confirmed.

Clinic Notes:
  • Stem cell transplant (40-70% die within 3 years-post transplant). Disease risk index is used to assess risk pre-transplant. Lack of predictors/biomarkers that can be measured post transplant. Adherent and rolling leukocyte be predictive of survival? DRI is a predictor of failure as well.
  • Continuous A&R Non-linear model: Cubic splines assumes no threshold so can't use it to show no better threshold. Only way thresholds can be valid is if the 2 populations on each side of the threshold have homogeneity. Odds ratios/survival curves arbitrarily move all over the place when adding high values/low values when dichotomizing (extremely sample dependent). Keeping items continuous gets rid of that.
  • Current literature is interpreting wrong, you want to contradict it. Choose one with lowest AIC from the 3 models. AIC tells you how powerful the fit is when you penalize it based on number of opportunities to not be flat. General principle is likelihood ratio is gold standard if not pre-specifying.
  • Recommend transforming heavily skewed distribution of A&R (cube root) then complete 3 models (linear, cubic spline 3 knot, cubic spline 4 knot). Using cube root and cubic splines in essence undoes the cube root in the end.

2021 January 04

Santiago Angaramo (Dolly Padovani-Claudio), Ophthalmology and Visual Sciences

Previous clinic session 10/12/2020, 12/14/2020

Project Title: Impact of Hypertension on Diabetic Macular Edema (DME) development among adult VUMC patients in the Synthetic Derivative Database with a prior diagnosis of diabetic retinopathy. Cohorts defined by mean BP: Normotensive, Prehypertension, Stage 1 HTN, Stage 2 HTN. This is my third biostats clinic for this project. Mentor confirmed.

Clinic Notes:
  • Should we reduce number of confounders? Should probably reduce number of categories on some variables (race/ethnicity)
  • RR or OR? Multivariable analysis would be much better. Univariate ignores all possible confounding so not too useful. Run risk of analysis changing between abstract and manuscript.
  • Significance does not really mean anything anymore. Would not present any univariate as it is not adjusted. Would only present test statistic and 95% CI if anything - not p-value.
  • Software for Multivariable analysis - R, Stata, SPSS
  • Medications in SD - not simple, can check with bioinformatics
Topic attachments
I Attachment Action Size Date Who Comment
BoxPlotR.RR BoxPlotR.R manage 5.7 K 17 Apr 2006 - 11:44 QingxiaChen  
InforegardingwhatmySPSSfilesays.docdoc InforegardingwhatmySPSSfilesays.doc manage 24.5 K 17 Apr 2006 - 11:44 QingxiaChen  
LOA_condensed_data.sxcsxc LOA_condensed_data.sxc manage 22.1 K 04 Dec 2006 - 09:17 PatrickArbogast Data from Edward Butterworth
Oluwole_Biostat_Clinic.xlsxls Oluwole_Biostat_Clinic.xls manage 46.5 K 25 Aug 2014 - 11:30 SharonPhillips data file for Olalekan Oluwole
StatisticalAnalysisRequest.docdoc StatisticalAnalysisRequest.doc manage 22.5 K 17 Apr 2006 - 10:26 QingxiaChen  
WellsIschemicCollat.pngpng WellsIschemicCollat.png manage 37.0 K 31 Jan 2011 - 13:58 MattShotwell  
WellsIschemicEF.pngpng WellsIschemicEF.png manage 37.4 K 31 Jan 2011 - 13:55 MattShotwell  
analysisEXT analysis manage 3.9 K 11 Feb 2006 - 20:30 QingxiaChen  
biost_clinic_stephanie_vaughn.csvcsv biost_clinic_stephanie_vaughn.csv manage 4.3 K 23 Apr 2007 - 11:37 PatrickArbogast  
biost_clinic_stephanie_vaughn.dtadta biost_clinic_stephanie_vaughn.dta manage 1.7 K 01 May 2007 - 11:12 PatrickArbogast Stata datafile for Stephanie Vaughn
biost_clinic_stephanie_vaughn.loglog biost_clinic_stephanie_vaughn.log manage 8.1 K 01 May 2007 - 11:13 PatrickArbogast Analysis results for Stephanie Vaughn from April 30th clinic
biost_clinic_stephanie_vaughn.xlsxls biost_clinic_stephanie_vaughn.xls manage 25.0 K 23 Apr 2007 - 11:37 PatrickArbogast  
boxplotdata.csvcsv boxplotdata.csv manage 2.7 K 17 Apr 2006 - 10:27 QingxiaChen  
clinicimage.jpgjpg clinicimage.jpg manage 134.8 K 14 Aug 2020 - 10:15 DalePlummer  
clintCarroll.sxcsxc clintCarroll.sxc manage 40.4 K 26 Feb 2006 - 21:30 FrankHarrell Clint Carroll Langerhans Data
clintCarrollabstract.sxwsxw clintCarrollabstract.sxw manage 8.7 K 26 Feb 2006 - 21:27 FrankHarrell Clint Carroll Langerhans Abstract
specificaims.docdoc specificaims.doc manage 25.5 K 13 Feb 2006 - 10:11 ChuanZhou Specific Aims
tang.rdarda tang.rda manage 13.4 K 19 Dec 2009 - 08:42 FrankHarrell Data from Yi Wei Tang processed using R code above
Topic revision: r730 - 21 Oct 2021, HeatherPrigmore
 

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