Curr Epidemiol Rep (2017) 4:271–280 https://doi.org/10.1007/s40471-017-0131-y PHARMACOEPIDEMIOLOGY (S TOH, SECTION EDITOR) 1,2 2 2,3,4 John W. Jackson & Ian Schmid & Elizabeth A. Stuart Published online: 6 November 2017 Springer International Publishing AG, part of Springer Nature 2017 Abstract can optimize sample size given design constraints, weighting Purpose of Review Propensity score methods have become estimators that asymptotically target matched and overlap commonplace in pharmacoepidemiology over the past de- samples, and the incorporation of machine learning to aid in cade. Their adoption has confronted formidable obstacles that covariate selection and model building. arise from pharmacoepidemiology’s reliance on large Summary These recent and encouraging advances should be healthcare databases of considerable heterogeneity and com- further evaluated through simulation and empirical studies, plexity. These include identifying clinically meaningful sam- but nonetheless represent a bright path ahead for the observa- ples, defining treatment comparisons, and measuring covari- tional study of treatment benefits and harms. ates in ways that respect sound epidemiologic study design. Additional complexities involve correctly modeling treatment Keywords Pharmacoepidemiology Comparative . . . decisions in the face of variation in healthcare practice and effectiveness Study design Non-experimental study dealing with missing information and unmeasured confound- Propensity score Causal inference ing.
Current Epidemiology Reports – Springer Journals
Published: Nov 6, 2017
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