Treatment Comparisons

Statistics · Axiom Academy

REAL WORLD Treatment Comparisons in Clinical Trials How ANOVA helps pharmaceutical companies and the FDA evaluate multiple treatments simultaneously The Challenge: Evaluating Multiple Treatments Imagine you're a biostatistician at a pharmaceutical company developing a new pain medication. You need to determine the optimal dose before seeking FDA approval. You've designed a randomized controlled trial with 200 patients suffering from chronic lower back pain. They're randomly assigned to one of four groups: Primary Outcome: Pain reduction on a 0-10 scale after 4 weeks of treatment. Why Can't We Just Do Multiple t-tests? With 4 treatment groups, you might think: "Let's just compare each pair of treatments using t-tests!" The Multiple Comparison Problem: With 4 groups, how many pairwise comparisons would we need? The FDA's Concern: Type I Error Inflation Each individual t-test has a 5% chance of a false positive (Type I error). But what happens when we do 6 tests? Probability of At Least One False Positive Family-wise Error Rate (FWER): Instead of a 5% chance of a false positive, we now have a 26.5% chance ! The FDA requires pharmaceutical companies to control the overall Type I error rate across all comparisons. This is why ANOVA is the standard approach for multi-arm clinical trials. ANOVA: Testing All Groups Simultaneously One-way ANOVA solves this problem by testing all groups at once with a single test that maintains the 5% Type I error rate.

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