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Research Methodology Insights
Practical guides on causal inference, study design, and statistical methods — written by researchers, for researchers.
Marginal Structural Models: A Practical Guide for Clinical Researchers
How MSMs use stabilized inverse probability weights to handle time-varying confounders — the ones that change over time and are affected by prior treatment. Covers weight estimation, model fitting, clinical examples, and common pitfalls.
Structural Causal Models & DAGs: A Practical Guide for Clinical Researchers
The causal framework behind every method you use. Covers DAGs, d-separation, do-calculus, backdoor/frontdoor criteria, mediation analysis, and how to draw the graph that makes your analysis work.
Inverse Probability Weighting: When PSM Discards Your Data
Why IPW outperforms matching by keeping all patients — and how extreme weights, positivity violations, and wrong variance estimators break published analyses silently.
Double Machine Learning: A Practical Guide for Clinical Researchers
How DML uses machine learning to estimate causal effects while controlling for high-dimensional confounders. Covers cross-fitting, Neyman orthogonality, clinical applications, and implementation in EconML.
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.
Regression Discontinuity Design: A Practical Guide for Clinical Researchers
RDD turns arbitrary thresholds into causal evidence. Covers sharp vs fuzzy designs, bandwidth selection, manipulation testing, clinical applications, and a complete reporting checklist.
Synthetic Control Methods: Building Counterfactuals When DID Fails
How to construct a synthetic twin from donor pools when parallel trends don't hold. Covers SCM optimization, validation via placebo tests, modern extensions (ASCM, SDID), and common pitfalls.
Difference-in-Differences: A Practical Guide for Clinical Researchers
When and how to use DID in clinical research. Covers parallel trends, staggered adoption, common pitfalls, reporting checklist, and modern estimators.
Instrumental Variables: When Observational Data Meets Unmeasured Confounding
When PSM and regression fail because of unmeasured confounding, IV methods offer a way forward. A practical guide covering instruments, LATE, Mendelian randomization, and the exclusion restriction.
Propensity Score Matching: A Practical Guide for Clinical Researchers
What PSM actually does, when it fails, and how to report it correctly. Written for researchers who want to use it — not just cite it.