Observational data for comparative effectiveness research: an emulation of randomised trials of statins and primary prevention of coronary heart disease

Abstract

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.

Figure 1

Flowchart of person-trials in the analysis, THIN ‘trials’ 2000-2006

Figure 2

Adherence to statin treatment by initiation status, THIN ‘trials’ 2000-2006

Figure 3

Impact of excluding varying periods of early follow-up (from 0 to 24 months) on the intention-to-treat hazard ratio (A), and the hazard ratio for continuous use vs. no use (B), THIN ‘trials’ 2000-2006 ^{a a}Vertical bars represent 95% confidence intervals. Early follow-up was excluded from the outcome model but not from the model used to estimate the IP weights.

Figure 4

Distribution of estimated propensity for treatment initiation by treatment status at the start of the THIN ‘trials’ 2000-2006