Clinical and Health Research Clinic

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Current Notes (2023)

2023 January 26

Megan Wright (Jessica Gillon), Pharmacy

My project is looking at the impact of requiring stop dates on antibiotic orders at the children's hospital. We have 4 time periods that we are looking at and would like help understanding what statistical tests should be run. Mentor confirmed.

2023 January 19

Stacy McIntyre (James Tolle), Pulmonary Critical Care

Our project is a retrospective chart review analyzing outcomes of cystic fibrosis (CF) patients who have transitioned from our pediatric clinic to our adult CF clinic. We would like to discuss biostats needed to evaluate associations between patient factors and outcomes. Mentor confirmed.

Sample of about 60

"Port CF" database -- deidentified excel sheet in OneDrive -- prospective registry

Data need to be cleaned (remove comments)

Biggest burning question: Do outcomes change after transition?

Analyze BMI using age as a covariate; subtract out the effect of age to look at effects of other vars

Try to avoid percentile approach; makes too many assumptions (linearity, normality)

- Stay as close to raw data as possible; pull original BMI data

Would learn a lot from spaghetti plot (red before transition, green after); can see data gaps

Tall and thin data set; date + BMI

Some patients will have less data after transition (one year) -> analysis will account for that

Problem with mean change = regression to the mean (someone could have good or bad day or measurement error)

Testing of significance must account for data pairing

Big picture: look at time continuously when possible

Living situation data not well defined; employment status, student status, & health insurance best we have (must deal with missingness, though)

Leon Scott, Orthopaedic Surgery

I want to set up a pilot study evaluating the effect of a low-energy diet (LED) intervention on measures related to weight, osteoarthritis, hypertension, and diabetes. The pilot study is to (a) test the intervention on a small scale before requesting funding for a sufficiently powered study and (b) ensure I have the infrastructure to execute the more extensive study effectively. My question for Biostatistics Clinic is, "are the statistical measures in my specific aims appropriate?"

Specific Aims and Hypothesis Aim 1: To evaluate the effect of an LED diet intervention, including pre-prepared meals, on weight. Hypothesis: Subjects will demonstrate a clinically significant reduction in weight (15%) at 12 weeks compared to their baseline. Approach: This aim is designed to compare mean differences in weight at the onset and endpoint of the study. As such, the data is from two paired datasets. The mean weight difference will have a normal distribution derived from a parametric variable. A paired t-test will be used to measure the difference between groups. Secondary outcomes will be the proportion of subjects that reach a 10% and 20% weight loss threshold. Too few subjects will be included to perform regression analysis of which variables (e.g., gender, the initial level of obesity, age) predict meeting those weight loss thresholds. This is a pilot study with five subjects. In the future, the sample size will be powered to determine a difference with a beta-error of 0.2 using a % weight loss standard deviation of 3.9%.

Aim 2: To evaluate the effect of diet intervention on knee osteoarthritis patient-reported outcomes measures. Hypothesis: Subjects will demonstrate a clinically significant improvement in the Visual Analog Score (VAS) for pain (2 points) at 12 weeks compared to their baseline. Approach: This study compares paired mean differences of pre- and post-VAS scores at the onset and endpoint of the study. Since the mean differences off a non-parametric VAS score have a non-normal distribution, a Wilcoxon-Rank-Sum Test will be used to measure the difference. Similar evaluation will be performed for secondary outcomes of KOOS sub-scales, WOMAC, and SF-12 scales. This is a pilot study with five subjects. In the future, the sample size will be powered to determine a difference with a beta-error of 0.2 using a VAS standard deviation of 1.1.

Aim 3: To evaluate if a LED diet intervention has a clinically significant change in markers of hypertension and T2D. Hypothesis: Between the onset of the study and the conclusion, subjects will experience improvements in systolic blood pressure, diastolic blood pressure, and HgbA1C. Regarding blood pressure, we hypothesize that 100% of the subjects will experience a 50% improvement in their baseline systolic and diastolic blood pressures and the goal of 120/80 mmHg. This compares baseline and endpoint datasets in a single population with non-normal distribution since we are evaluating proportions that meet a blood pressure goal, not the blood pressure numbers themselves. The statistical measure that will show significant change is a Wilcoxon-Rank-Sum test. Regarding the HgbA1C, we hypothesize an average 1.0% point change at three months. Our statistical measure for if the group demonstrates this degree of change is a paired t-test (the expected standard deviation for a 1.29% change is 1.32%).

Aim 4: To evaluate if a LED diet intervention, including pre-prepared meals, reduces the proportion of patients using non-protocol interventions. Hypothesis: The proportion of subjects that use a non-protocol intervention (e.g., oral/topical NSAIDs, other oral/topical analgesics, corticosteroid injections in the previous three months, braces, units of short-acting insulin & units of long-acting insulin, etc.) will be lower and reach statistical significance (p<0.05) after the intervention compared to those subjects paired values at the onset of the study. Approach: This aim is to compare two proportions at various time points with two data sets. Statistical significance will be measured using the McNemar test. Furthermore, using non-protocol interventions will be evaluated to see if they predict the clinical changes in patient-reported outcomes and weight using logistic regression.

Sample size of 300 at minimum for binary outcomes

- Scatterplot?

Paired t-test would be fine; Wilcoxon signed rank test more robust

BMI > 35 to be included in study

- Regression to the mean an issue (caught person on good/bad day, measurement error)

Admit patients if maintained stable BMI for a particular period of time? Unstable correlates with having higher/lower BMI...

Fidelity to the diet is less of a concern

Example: reliability of self-reported food intake = not good

Recalling intake might increase possibility of cheating

Signed rank test will work for aim 2

Wilcoxon rank difference test good for paired data -- same p-value no matter how you transform the data; robust

Rank difference test for aim 3

- Make patient their own control; pre-post, not against 120/80

- Wait x minutes, measure; wait, measure; use same instrument, keep other factors constant

Meals will be delivered to participant's house

2023 January 12

Doug Bryant (W. Evan Rivers), Physical Medicine and Rehabilitation

Endoscopic rhizotomy systematic review - follow up from last meeting on May 5th, 2022. Data collection is complete, would like to further discuss VICTR application process. Mentor confirmed.

Six studies identified that met inclusion criteria - these were determined to meet acceptable standard of care

Pre-procedural screening

Any population-level differences across the studies should be adjusted for

Are studies randomized? Of the ones with comparison groups, one was randomized, others were cohort studies

Next steps: assess amalgomated effect

Meta analysis can properly account for study to study variation

Simple pooled analysis (CI will be falsely narrow)

- How should you weight?

Accounting for Time Zero across studies

Randomized trials = the best

"Surgery before?" could be key covariate

Nail down grouping and modeling in further dialogue

- Make goals, candidate studies, and assessed outcomes clear

VICTR voucher good for a year

"Spreadsheet from hell" on website: things to avoid

Brett Kroncke, Medicine/Clin Pharm

Testing genetic features' ability to predict risk of cardiac events. Recorded data are age at first event, frequency of subsequent events (some are age at subsequent event), and use (start date and duration) of controlling medication. I would like to use these data to evaluate the ability of genetic features to predict these events, controlling for other clinical features and use of medications.

Predict risk of event given carrier heterozygous status

Control for known clinical features: corrected QT interval, age at first event, event rate, age at all subsequent events, age at Beta blocker use, ...

100 people might have genetic variant (no variation in that marker)

- Like dose-response relationship without linearity assumption

How to handle multiple, distinct events (largely one type: syncope)?

- State transition model, allowing patients to move in and out of various states

If data are sparse (most don't have syncope, and if they do, only once)

- Cox model (time to event)

Frank: "why your collaborator is wrong": https://hbiostat.org/glossary

- Look under "dependent variable" and click on the "other information" tab under that.

Explore imputation approaches

Key issue in arrhythmia research: access to EKG or summary of EKG

- Barrier will be quite high, but payoff could be worth it