Observational Studies
Everything on Aqrab tagged Observational Studies — grouped into one landing page so readers can go deeper by problem family instead of bouncing around the archive blind.
Bias Amplification: When Adjustment Makes Unmeasured Confounding Worse
A practical guide to bias amplification for clinical researchers. Covers near-instruments, noisy severity proxies, treatment-prediction traps, and why the wrong adjustment variable can magnify residual confounding instead of reducing it.
Selection Bias: When Your Study Sample Is the Problem
A practical guide to selection bias for clinical researchers. Covers referral filtering, survivor bias, complete-case analysis, informative loss to follow-up, collider-driven selection, and why a clean model cannot rescue a distorted sample.
Confounding by Indication: When Sicker Patients Make Treatments Look Dangerous
A practical guide to confounding by indication for clinical researchers. Covers treatment selection, severity-driven prescribing, contraindication bias, why routine adjustment often fails, and how to design observational comparisons that do not confuse prognosis with treatment effect.
Proximal Causal Inference: What to Do When Unmeasured Confounding Is Still on the Table
A practical guide to proximal causal inference for clinical researchers. Covers proxy variables, treatment-inducing versus outcome-inducing proxies, bridge functions, completeness, and why this method is powerful but brutally assumption-heavy.
Negative Controls: The Bias Check Most Observational Studies Skip
A practical guide to negative control outcomes and exposures for clinical researchers. Covers residual confounding, selection bias, surveillance bias, falsification endpoints, and how to interpret a failed negative control without lying to yourself.
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.
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