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.
The Denominator Illusion: Why 20,000 Measurements May Still Mean 200 Patients
A practical unit-of-analysis guide for clinical researchers. Separate rows, patients, clusters, and target populations before repeated observations create false precision.
Ecological Fallacy: When Hospital-Level Data Become Patient-Level Advice
A practical ecological fallacy guide for clinical researchers. Match the unit of analysis, exposure, outcome, and claim before turning group-level associations into patient-level advice.
Crossover Trials: When Every Patient Is Their Own Control—and Their Own Carryover Problem
A practical guide to crossover trials for clinical researchers. Audit treatment reversibility, washout, carryover, period effects, sequence, dropout, and paired analysis before trusting an efficient within-patient comparison.
Causal Identification: Why Longitudinal Data Still Need a Design
A practical causal identification guide for longitudinal clinical research. Learn why measurement order does not create comparability, what repeated observations add, and how to audit causal claims with five reviewer questions.
AI Before–After Studies: When Faster Care Is Not Yet an AI Effect
A practical guide to evaluating healthcare AI after deployment. Audit pre-trends, concurrent comparisons, co-interventions, outcome measurement, and the claim ceiling of before–after and interrupted time-series designs.
Separation in Logistic Regression: When Perfect Prediction Breaks the Odds Ratio
A practical guide to separation in logistic regression for clinical researchers. Learn why sparse or perfectly predictive data produce infinite estimates, how to diagnose the failure, and what reviewers should demand before trusting a dramatic odds ratio.
Interval Censoring: When the Visit Date Pretends to Be the Event Date
A practical interval censoring guide for clinical researchers. Learn why periodic assessments create event-time windows, how naive date substitution manufactures precision, and what reviewers should demand before trusting time-to-event claims.
Before the Model: A Five-Gate Study Design Audit for Clinical Research
A practical five-gate study design audit for clinical researchers. Check the question, population, comparison, measurement, and follow-up before a sophisticated model gives a fragile design false confidence.
Causal Readiness: When a Huge Linked Dataset Still Cannot Identify an Effect
A practical guide to causal readiness in linked health and administrative data. Learn why scale and propensity-score overlap are not enough when treatment, need, comparators, or outcomes are poorly measured.
Small Number of Clusters: When 500 Patients Still Behave Like 10 Sites
A practical guide to inference with a small number of clusters in clinical research. Learn why patients inside the same site are not independent evidence, why default cluster-robust standard errors can be too optimistic, and what reviewers should demand before trusting a cluster-level result.
Estimation Over Testing: Four Habits That Quietly Break Clinical Statistics
A practical critique of four entrenched habits in clinical biostatistics — significance testing where the real question is estimation, prediction accuracy scored far from the bedside, meta-analysis on autopilot, and the false precision of point estimates that assume away what cannot be tested — with an interactive explorer that reads one trial three ways and what to do instead.
The Will Rogers Phenomenon: When Better Staging Improves Every Group and Nobody Lives Longer
A practical guide to the Will Rogers phenomenon (stage migration) for clinical researchers. Covers why sharper classification can lift survival in every stage while overall survival stays flat, how to separate stage migration from real progress, how it differs from lead-time bias and overdiagnosis, and what reviewers should demand when outcomes are compared across eras or cohorts.
Informative Cluster Size: When the Biggest Sites Start Writing the Result
A practical guide to informative cluster size for clinical researchers. Covers why larger centers can quietly dominate treatment effects, how weighting changes the estimand, and what reviewers should demand before trusting clustered results.
Nested Case-Control Design: When Cheap Control Sampling Still Has to Respect Event Time
A practical guide to nested case-control design for clinical researchers. Covers incidence-density sampling, risk-set control selection, how it differs from case-cohort design, and what reviewers should demand before trusting the result.
Repeated Eligibility in Target Trial Emulation: When One Patient Quietly Enters the Trial Again
A practical guide to repeated eligibility in target trial emulation for clinical researchers. Covers re-entry rules, overlapping follow-up, carryover, and what reviewers should demand before trusting a sequential-trial analysis.
Case-Cohort Design: When Measuring Everyone Is the Wrong Expense
A practical guide to case-cohort design for clinical researchers. Covers when a random subcohort is more honest than measuring everyone, how it differs from nested case-control sampling, and what reviewers should demand before trusting the result.
Case-Time-Control Design: When Case-Crossover Starts Confusing Time Trends with Treatment Effects
A practical guide to the case-time-control design for clinical researchers. Covers exposure-time trends, referent sampling, protopathic bias, and what reviewers should demand before trusting a self-matched trigger analysis.
Triangulation in Clinical Research: When One Elegant Design Still Leaves the Same Blind Spot
A practical guide to triangulation in clinical research. Covers what counts as genuinely complementary evidence, how to map designs to specific threats, and what reviewers should demand before trusting “robustness” claims.
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.
Baseline Covariate Windows: When “Pre-Treatment” Variables Arrive Fashionably Late
A practical guide to baseline covariate windows for clinical researchers. Covers lookback periods, stale severity measures, same-day contamination, confounding capture, and what reviewers should demand before trusting “adjusted” observational results.
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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