Data Processingawslabs/hcls-agent-skills
pharmacoepidemiology
Reason about pharmacoepidemiologic study design for causal inference from real-world data — choosing active comparator new-user designs, emulating target trials, avoiding immortal time bias, handling time-varying confounding with marginal structural models, and selecting propensity score methods. Use when the user asks to design a drug safety or effectiveness study, choose between propensity score matching vs weighting vs stratification, emulate a target trial, handle immortal time bias, apply marginal structural models, assess unmeasured confounding with E-values, select an active comparator, define a new-user cohort, or evaluate a pharmacoepidemiology study design. Triggers include "active comparator", "new-user design", "target trial emulation", "immortal time bias", "propensity score matching", "IPTW", "marginal structural model", "confounding by indication", "E-value", "negative control outcomes", "quantitative bias analysis", "time-varying confounding", "prevalent user bias", "washout period", "landmark analysis", "stabilized weights".