Observational data for comparative effectiveness research: an emulation of randomised trials to estimate the effect of statins on primary prevention of coronary heart disease

By G. Danaei, L. A. García Rodríguez, O. Cantero, R. Logan, Miguel A. Hern'an. 2011

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This article reviews methods for comparative effectiveness research using observational data. The basic idea is using an observational study to emulate a hypothetical randomised trial by comparing initiators versus non-initiators of treatment. After adjustment for measured baseline confounders, one can then conduct the observational analogue of an intention-to-treat analysis. We also explain two approaches to conduct the analogues of per-protocol and as-treated analyses after further adjusting for measured time-varying confounding and selection bias using inverse-probability weighting. As an example, we implemented these methods to estimate the effect of statins for primary prevention of coronary heart disease (CHD) using data from electronic medical records in the UK. Despite strong confounding by indication, our approach detected a potential benefit of statin therapy. The analogue of the intention-to-treat hazard ratio (HR) of CHD was 0.89 (0.73, 1.09) for statin initiators versus non-initiators. The HR of CHD was 0.84 (0.54, 1.30) in the per-protocol analysis and 0.79 (0.41, 1.41) in the as-treated analysis for 2 years of use versus no use. In contrast, a conventional comparison of current users versus never users of statin therapy resulted in a HR of 1.31 (1.04, 1.66). We provide a flexible and annotated SAS program to implement the proposed analyses.

Subfields: Economics and Econometrics, Statistics and Probability, Surgery

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Citations in the corpus

Handling time varying confounding in observational research (2017, background): “16 Time varying confounding affected by past exposure also exists when there is feedback between exposure and outcome.”
Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses (2016, background): “nested trials with increasing time zero [17,18], requires appropriate variance adjustment.”
Practice of Epidemiology Propensity Score Methods for Analyzing Observational Data Like Randomized Experiments: Challenges and Solutions for Rare Outcomes and Exposures (2015, methodology): “(8) and developed in parallel by Schaubel et al.”
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. (2016, background): “, a month) and emulate a new trial starting at each time unit (34).”