Study Design
Everything on Aqrab tagged Study Design — grouped into one landing page so readers can go deeper by problem family instead of bouncing around the archive blind.
AI-Assisted Methods Review: What LLMs Can Catch, What They Cannot, and Where Judgment Still Matters
A practical guide to AI-assisted methods review for clinical researchers. Covers where LLMs help with structural critique, where source verification and causal judgment still require humans, and what reviewers should demand before trusting AI-generated methodological comments.
Run-In Periods: When Your Trial Randomizes the Easy Patients First
A practical guide to run-in periods for clinical researchers. Covers adherence enrichment, tolerability selection, estimand drift, external validity, and what reviewers should demand before trusting a polished randomized cohort.
Washout Periods: When “New Use” Is Just Old Use with Better PR
A practical guide to washout periods for clinical researchers. Covers new-user definitions, refill cycles, intermittent treatment, data-history limits, and what reviewers should demand before trusting an incident-user cohort.
Exposure Lagging: When Your Induction Window Becomes Wishful Thinking
A practical guide to exposure lagging for clinical researchers. Covers induction periods, reverse causation, protopathic bias, estimand drift, and what reviewers should demand before trusting a lagged analysis.
Grace Periods in Target Trial Emulation: Clinical Realism or Future Information in Disguise?
A practical guide to grace periods in target trial emulation for clinical researchers. Covers when a grace window is defensible, when it becomes immortal time in formalwear, and what reviewers should demand before trusting the result.
Prevalent-User Bias: When Your Drug Study Starts After the Interesting Harm Already Happened
A practical guide to prevalent-user bias for clinical researchers. Covers depletion of susceptibles, survivor selection, post-treatment baseline covariates, and what reviewers should demand before trusting late-entry treatment cohorts.
Clone-Censor-Weight: The Target Trial Fix That Still Breaks When You Use It Casually
A practical guide to clone-censor-weight for clinical researchers. Covers when the design is needed, how cloning and artificial censoring work, where immortal time bias reappears, and what reviewers should demand before trusting a target trial emulation.
Prediction vs Causation: Why Your Best Risk Model Still Cannot Tell You What to Treat
A practical guide for clinical researchers on the difference between prediction and causation. Covers why strong risk models do not identify treatment effects, how to frame the right estimand, and what reviewers should flag in AI-driven clinical studies.
Case-Crossover Design: When Patients Become Their Own Controls
A practical guide to case-crossover designs for clinical researchers. Covers self-matching, hazard versus control windows, transient exposures, protopathic bias, time trends, and when this elegant design is exactly right or exactly wrong.
Multiple Imputation: Missing Data Does Not Become Innocent Because MICE Ran
A practical guide to multiple imputation for clinical researchers. Covers MICE, complete-case failure, MAR versus MNAR, imputation-model design, and why missing data needs causal thinking instead of software ritual.
Estimands: The Causal Question You Should Define Before Running the Analysis
A practical guide to estimands for clinical researchers. Covers treatment strategies, intercurrent events, target populations, summary measures, and why many studies fail because they never define the actual causal question clearly.
Active Comparator New-User Design: The Observational Study Upgrade Most Drug Papers Need
A practical guide to the active comparator new-user design for clinical researchers. Covers why treated-versus-untreated comparisons fail, how new-user cohorts reduce prevalent-user bias, how active comparators narrow confounding by indication, and what reviewers should demand before trusting comparative effectiveness claims.
Immortal Time Bias: The Fake Survival Advantage Hiding in Bad Study Design
A practical guide to immortal time bias for clinical researchers. Covers time zero, future-based exposure definitions, delayed treatment initiation, target trial emulation, and why you cannot adjust your way out of a broken timeline.
Transportability & External Validity: When Your Causal Estimate Travels, and When It Absolutely Does Not
A practical guide to transportability and external validity for clinical researchers. Covers target populations, effect heterogeneity, trial selection, overlap, reweighting, and why “generalizable” is usually a lazy claim unless you prove the estimate can actually travel.
Interference & Spillover Effects: When One Patient's Treatment Changes Another's Outcome
A practical guide to interference and spillover effects for clinical researchers. Covers SUTVA violations, direct versus indirect effects, partial interference, cluster and network designs, and why contamination is often the estimand trying to get your attention.
DAG Construction: How to Draw a Causal Graph Before You Touch the Model
A practical guide to DAG construction for clinical researchers. Covers time ordering, node selection, confounders vs mediators vs colliders, minimally sufficient adjustment sets, and why most covariate lists are just causal confusion wearing a regression badge.
Target Trial Emulation: A Practical Guide for Clinical Researchers
The framework that bridges observational data and causal claims — by asking what RCT you wish you had. Covers protocol specification, time zero alignment, clone-censor-weight, immortal time bias, and reporting.
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