Pharmacoepidemiology Study Design

Overview

This skill teaches the agent how to think about pharmacoepidemiologic study design — the discipline of estimating causal drug effects from non-randomized real-world data (claims, EHR, registries). The core challenge is confounding: patients who receive Drug A differ systematically from those who receive Drug B, and naive comparisons conflate treatment effects with selection effects.

A well-designed pharmacoepidemiology study addresses this through three pillars: (1) a new-user active comparator design that aligns time zero and reduces confounding by indication, (2) propensity score methods or target trial emulation to balance measured confounders, and (3) sensitivity analyses to probe the impact of unmeasured confounding.

Use this skill to interrogate study designs, identify bias sources, and guide the user toward defensible causal inference before analysis code is written.

Usage

Invoke this skill when the user: