ABSTRACT
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ABSTRACT This chapter reviews key statistical tenets of comparative effectiveness research with an emphasis on the analysis of observational cohort studies. The main concepts discussed relate to those for causal analysis whereby the goal is to quantify how a change in one variable causes a change in another variable. The methodology for binary treatments and a single outcome are reviewed; estimators involving propensity score matching, stratification, and weighting; G-computation; augmented inverse probability of treatment weighting; and targeted maximum likelihood estimation are discussed. A comparative assessment of the effectiveness of two different artery access strategies for patients undergoing percutaneous coronary interventions illustrates the approaches. Rudimentary R code is provided to assist the reader in implementing the various approaches.
