Estimating the effects of two classes of drugs on hemoglobin with a doubly robust method

With observational data, the effect of the exposure is often confounded by baseline covariates. Doubly robust estimation utilizes models for the exposure and for the outcome to estimate the causal effect of an exposure on an outcome. In a retrospective study designed to evaluate change in hemoglobin (Hgb) with use of angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB), in a primary care patient population, we found that receiving ARB vs. ACEI was associated with several of the baseline covariates that were chosen a priori and thought to be informative in estimating follow-up (F/U) Hgb. We therefore adopted the doubly robust semiparametric efficient estimator of Robins et al. (1994) in a causal analysis of the treatment effect of ARB vs. ACEI on F/U Hgb. The method produces an estimate of the treatment effect by simultaneously incorporating the propensity of a subject to receive ACEI or ARB, given their levels of covariates, and the effects of the covariates upon the response of interest, F/U Hgb. It is doubly robust in the sense that it produces an unbiased estimate of the treatment effect if either the outcome or propensity model is correct. Following a complete-case ANCOVA, we found the estimated F/U Hgb and bootstrap bias-corrected accelerated (BCa) 95% confidence interval (CI) of ACEI and ARB to be 14.31 (14.21, 14.42) gm/dL and 14.48 (14.33, 14.62) gm/dL, respectively. The causal effect of ARB relative to ACEI and associated BCa CI is estimated to be 0.17 (0.00, 0.31) gm/dL (p = 0.0310).