Reducing the variance of the prescribing preference-based instrumental variable estimates of the treatment effect

Abstract

Instrumental variable (IV) methods based on the physician's prescribing preference may remove bias due to unobserved confounding in pharmacoepidemiologic studies. However, IV estimates, originally defined as the treatment prescribed for a single previous patient of a given physician, show important variance inflation. The authors proposed and validated in simulations a new method to reduce the variance of IV estimates even when physicians' preferences change over time. First, a potential "change-time," after which the physician's preference has changed, was estimated for each physician. Next, all patients of a given physician were divided into 2 homogeneous subsets: those treated before the change-time versus those treated after the change-time. The new IV was defined as the proportion of all previous patients in a corresponding homogeneous subset who were prescribed a specific drug. In simulations, all alternative IV estimators avoided strong bias of the conventional estimates. The change-time method reduced the standard deviation of the estimates by approximately 30% relative to the original previous patient-based IV. In an empirical example, the proposed IV correlated better with the actual treatment and yielded smaller standard errors than alternative IV estimators. Therefore, the new method improved the overall accuracy of IV estimates in studies with unobserved confounding and time-varying prescribing preferences.