Trial Design
Everything on Aqrab tagged Trial Design — grouped into one landing page so readers can go deeper by problem family instead of bouncing around the archive blind.
Adaptive Enrichment Trials: When Precision for One Subgroup Pretends to Be Evidence for Everyone
A practical guide to adaptive enrichment trials for clinical researchers. Covers predictive versus prognostic enrichment, assay timing, multiplicity, external validity, and what reviewers should demand before trusting a biomarker-selected win.
Multiple Testing in Clinical Trials: When One Positive Endpoint Is Just the Loudest Coin Flip
A practical guide to multiple testing in clinical trials for clinical researchers. Covers endpoint families, subgroup fishing, interim looks, alpha control, and what reviewers should demand before trusting a lone positive result.
Early Stopping for Benefit: When a Trial Quits While the Effect Is Still on Its Best Behavior
A practical guide to early stopping for benefit in clinical trials. Covers interim looks, alpha spending, exaggerated effect sizes, immature follow-up, and what reviewers should demand before trusting a triumphant stop.
Subgroup Analysis: When “Personalized” Findings Are Mostly Multiplicity Wearing a Stethoscope
A practical guide to subgroup analysis for clinical researchers. Covers interaction testing, multiplicity, power failure, post hoc storytelling, and what reviewers should demand before trusting treatment-effect heterogeneity claims.
Composite Endpoints: When One Trial Outcome Quietly Becomes Four Different Clinical Questions
A practical guide to composite endpoints for clinical researchers. Covers when endpoint bundles improve efficiency, when they distort clinical meaning, how soft components hijack results, and what reviewers should demand before trusting the headline.
Per-Protocol Effects: The Estimand Everyone Wants and the Bias Trap They Usually Build
A practical guide to per-protocol effects for clinical researchers. Covers sustained-adherence estimands, naive as-treated failure, selection bias after protocol deviation, and what reviewers should demand before trusting per-protocol claims.
Explore more topics
These are ranked by how often they appear alongside Trial Design, so the next click is more likely to be useful than random.