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Notes 2016

- Recommend a randomized cross-over study design with double blinding if possible
- Select a side-effect measurement tool
- Clearly state inclusion/exclusion criteria

- "I would like to request some time to talk to another statistician about exploratory factor analysis I am doing in R with the psych package. This procedure is fairly new to me and I have some questions that I would like help with."

- Association between ITSP and illness severity score
- Association between parenting style (PSDQ) and infant adoptation.

- Bladder neck size on incontinence, controlling for BMI, age, preop score, disease status, and stitch.
- Restricted cubic spline examples: MSCI Biostat II STATA

- I'm working on a project involving longitudinal data with children who stutter and persist, children who stutter and recover, and children who do not stutter.

- Matched design, 1:1, 1:many, BOOM
- match on socio-economic, clinical factors, etc.

- Change point analysis
- see if readmission rates change at time of policy implementation

- REQUEST FOR VICTR SUPPORT: Clinic statisticians recommend a 90 hour voucher.

- Developing a randomized controlled clinical trial in mental literacy. Working notion, to increase mental literacy, communications which in turn increase mental health outcomes.
- Submit concept paper to NIMental Health. Questions to address and want to get statistical expertise.
- Questions: 4 educational arms and a control group for a total of groups. Setting community mental health clinics

- Consider cluster randomization. Figure out how many clinics that you will have access to. Five arms note one clinic receive one arm.
- How to assess "fidelity"? Recording data consistently. Approach with assessment for some of inter-rater reliability.

- Mediation analysis (Baron & Kenny, structural equation modeling). First you need to show that your intervention has an association with response variable. Mediator will be communication for example

- * (Y~X) Education is associated with improved mental health.
- * (X~M) Education works through health literacy and/or communication(Mediators) to improve mental health.
- Will I benefit from cross-over design? We believe that once knowledge is gained it will be difficult to have a "wash out". Cross over design will be more appropriate to a set up such the development of new drug with clear wash out.
- Question from biostatisticians: do you need 4 arms? Can you combine some of these educational programs.
- Transient effect: Is it common in the literacy literature and look into other clinical studies such as in diabetes which require behavioral changes. There are issues of relapse and maintaining adherence.
- Timeline: Extend two years follow up time to address the "transient effect" although most studies have short follow up. Can you follow up subjects on StarPanel to show that you can address long term effects. Need to sit down with statitiscians to address realistically the multiple issues. How many clinics do you think that you could have access to? Recruitment time? How many subjects are needed?
- Consider short term effects and long term outcomes. Can you design you study pragmatically without too much effort to collect data? Using the real set up Dr. entries for follow up assessment.
- Recommendation: Follow up with VICTR voucher and statistician for help with proposal.

- Survey - baseline assessment - residents and attendings - 85 questions
- Additional survey after rotation

- Children previously seen - diagnostic visit; 4y ago; stuttering up to 18m; English is primary language
- New follow-up for status at one point in time
- Baseline variables that originate from continuous measurements (e.g., age at onset) need to be analyzed as continuous variables
- Include baseline stuttering severity as a predictor
- With a maximum of 150 children the maximum number of candidate predictors might be around 10 if the outcome variable is almost continuous (it's worse if outcome is almost binary)
- Stuttering is multi-dimensional, e.g., some children may reduce amount of speaking because of the problem, so they seem to stutter less
- May consider a compound summary of all the outcome measures, e.g., average rank across children; clinical ranking of scenarios can also be used
- Dependent variable needs to have at least 5 frequently levels and be ordered or continuous
- If there is one standout, popular scale, that one could be used by itself
- Empirical variable selection requires an enormous sample size to reliably find the "right variables" so it's best not to use selection procedures; can find various approximations to the model for clinical non-computerized application
- Data reduction methods (variable clustering, principle components, redundancy analysis) can be useful for effectively reducing the number of predictors to use in the multivariable model

- To go over analysis produced by VICTR biostatisticians

- Best to present all the raw data
- Might use 3 quartiles (25th and 75th percentiles and median) as descriptive stats and use Wilcoxon signed rank test for testing for a difference between baseline and 4h
- There's also two types of samples - same study repeated with different samples, sample drug concentration
- Only have 2 patients; plan to have 5 later
- Better to not average over the 3 replicates - may hide variability
- Bland-Altman plot (mean-difference plot) is a good way to show agreement and whether variation is stable over base levels. If band of variability expands going from left to right, this is an indication that perhaps the analysis should be done on the log concentration scale.
- Other useful ways to summarize data: mean absolute difference between estimated and true concentrations - separately by no gel and gel
- Can also show mean absolute differences between replicates ignoring the true concentrations
- There are problems with lower limit of detection, representing missing values that are not randomly missing; ordinary analysis may be problematic

- Interested in variation over time within patient
- Variants are summarized into polygenetic risk scores
- Difficulty in interpreting results if patients are being treated for the lab abnormality being studies
- How to define time zero?
- May want to ignore records corresponding to post-Rx periods
- Started with HDL
- Side study: confirm that med initiation that is supposed to modify HDL really does
- Simplest longitudinal analyses:
- Compute within-patient Gini's mean difference to correlation with gen. risk score; asks whether gen. risk is correlated with variability
- Similar but summarize with the median to correlate gen. risk with overall height of the longitudinal records
- Summarize entire longitudinal record with slope and intercept; AUC and relate summary measures to gen. risk score

- Would be useful to summarize the data using representative patients after clustering on mean HDL, shape, number of observations, maximum time gap between any two measurements
- Another type of analysis: summarize each patient using the 9 deciles of HDL; use these deciles to predict polygen. risk score
- Does not take time ordering into account
- Might add a slope or shape summary to the deciles

- I am a physical therapist in the Sports Medicine outpatient department and we are planning two studies that we would like to discuss. Primarily, though, we would like to discuss a prospective observational study we will be performing this coming school year with overhead athletes – we will be looking at the relationship of core strength to the likelihood of shoulder injury in overhead athletes. We plan to test the athletes’ core strength at start of their season and then collect data on injuries and time lost from playing their sport during the season. Specifically, we have questions about what our number of subjects should be in order to determine a difference and what we will need to do statistically in order to analyze the data.
- Outcomes: number of days (or proportion) lost during the season due to shoulder injuries
- Need information on the proportion of athletes who would get shoulder injury during a season. Sample size needed would be large if the proportion is very low.
- Could use logistic regression to examine association between core strength and incidence of injury
- Consider other factors that could affect shoulder injury such as the type of sport, number of years practicing, etc. These factors can be adjusted for in the regression model.
- To calculate the sample size, need to specify the outcome, type of analysis used, the meaningful difference (effect size: odds ratio of injury upon one unit change in core strength) you want to detect, and some preliminary data on the outcome measurements (rate or variation). A rule of thumb: 20 cases of injury are needed for each factor you'd like to analyze.
- Consider choosing a type of sports with the greatest association between core strength and shoulder injury.
- how to quantify core strength, a single summary score?
- A second study I am wondering about is an Anterior Cruciate Ligament Reconstruction study where we are going to compare a group of patients in a home based program versus standard care (control). We are wanting to do a feasibility study this year in our clinic, and I think it will be a prospective case-control study, or maybe prospective cohort—we also want to know about N size and analysis after ward.
- Enroll 7 patients in one month. Feasibility study.

- Parkinson's disease - norepinephrine; VICTR application
- Original intention peripheral blood pressure support
- Interested in a combined medication regiment
- Goal to get nor. into CNS
- Propose to study n=16 patients
- Need dose titration 100mg bid -> 600mg 3/day
- Which dose do patients tend to end up with?
- Is a safety & tolerability study, partly dose-finding
- Patient response that is monitored is blood pressure - minimizing orthostatic symptoms without side effects; target supine BP plus headaches, dizzyness, mania; symptoms are of primary emphasis
- Is there an accepted symptom summary scale? If not may need to just count the number of symptoms present
- But dose adjustments are clinical adjustments based on a symptom "gestalt"
- Target for analysis is final dose
- Need SD of dose; best available data will probably come from what doses are used long-term in clinical practice; we'll assume this is a stand-in for the final tolerable dose
- Once a useful SD estimate is found, it can be used to compute the likely margin of error in estimating the population mean required dose when n=16, with say 0.95 confidence. The margin of error is the half-width of the confidence interval.
- Would be good to know what evidence exists for the usefulness of plasma drug concentrations in estimating the final required dose

- PQI project. Two types of images (new vs. old method) were performed for each patient.
- Examine the agreement between the two methods based on the paired data (kappa stat). Readings are ordinal values.
- Let a few radiologists read the two sets of images in random order to study the agreement.
- May need a couple of hundreds of patients, and a few (2 to 6) radiologists. (also want to have good agreement between radiologists, that is, readings of a certain method do not heavily depend on the experiences of radiologists).

- Mary-Margaret Fill, TDH EIS
- Neonatal abstinence syndrome and long term outcomes
- Merge TennCare data with educational data
- Suggest regression model with traditional covariate adjustment unless need to do special matching (family, neighborhood)
- Biggest assumptions: children move away from TN for reasons unrelated to potential educational achievement
- Confounding: women giving birth to infant with NAS may tend to be different from those not having an NAS child; need to adjust for all factors related to this that might be associated with educational outcome
- Also what is the effect of school on test scores?
- Birth records have mother's educational level, zip code, tobacco use
- Matching records may be challenged by mother changing last name
- Might also look at infant and mother utilization of services, diagnosis of ADHD, etc.; cross-correlate with educational achievement

- See Chapter 8, P. 8-12 of http://fharrell.com/doc/bbr.pdf - suggest using the r=0 curve. This approach is using the margin of error based on 0.95 confidence limits. E.g.: "With a sample size of N subjects we can estimate the correlation coefficient between two variables to within a margin of +/- xx with 0.95 confidence (see graph)."
- Important to prioritize the comparisons and to report them in this pre-specified order so that no multiplicity corrections will be needed
- A regression model that allows for interaction between time since trauma and amount of trauma would allow for estimation of the time-decay or enhancement of memories-effect. The time interaction effect may be nonlinear.

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