Biostatistics for Non-Statisticians: Understanding Different Types of Analyses and When to Use Each

This is tailored to non-statistician clinical trial professionals who wish to gain a better understanding of the various types of statistical analyses used in clinical trials. How do you choose the right statistical analysis for your clinical trial—and what do those results really mean? In this webinar, SDC breaks down core biostatistical concepts for non‑statistician clinical trial professionals. Using practical, real‑world examples, this session demystifies commonly used analyses and explains when—and why—to use each method to support confident, data‑driven clinical decisions. What You’ll Learn: • What statistical inference is and how hypotheses drive clinical trial conclusions • How to interpret p‑values, test statistics, and variability • The difference between nominal, ordinal, and continuous data • When to use t‑tests vs. analysis of covariance (ANCOVA) • How adjusting for baseline can affect variability and power • Why the Wilcoxon rank‑sum test is often misunderstood—and what it truly tests • How to analyze binary outcomes using Pearson chi‑square and logistic regression • How time‑to‑event data is evaluated using Kaplan–Meier curves, log‑rank tests, and hazard ratios • Key strategies for improving study efficiency without increasing sample size Learn more about SDC at: https://bit.ly/3NUU9Oo 0:00 Introduction 0:26 Webinar Housekeeping 1:48 Dale W. Usner, CSO & SVP Strategic Scientific Consulting 3:51 Agenda 4:42 General Objective of a Pivotal Clinical Trial 5:42 Efficacy and Safety Clinical Trial 7:06 Statistical Inference through Hypotheses 9:17 Statistical Inference p-values 11:25 Types of Data Collected (Continued) 13:55 Continuous (Quantitative) Data Example 14:29 Continuous Data Example Continued 17:00 Distribution of Mean (N=1) Day 90 Values 18:13 Distribution of Mean (N=100) Day 90 Values 23:14 Observed Day 90 Values (n = 50 / tx) 25:09 Statistical Inference Using t-test 27:57 Analysis of Covariance: Adjusting for Baseline 31:41 Statistical Inference Adjusting for Baseline 37:43 Wilcoxon Rank Sum (Mann-Whitney U) Test 41:10 Wilcoxon Rank Sum Test - Data Distributions 42:49 Quantitative Data Example Binary Outcome (Ordinal Measure) 45:06 Binary Outcome: Observed 45:40 Binary Outcome: Pearson XP Statistic 48:25 Logistic Regression Adjusting for Baseline 52:43 Quantitative Data Example: Time to Event