A comparison of mixed-effects quantile stratification propensity adjustment strategies for longitudinal treatment effectiveness analyses of continuous outcomes

The propensity adjustment is used to reduce bias in treatment effectiveness estimates from observational data. We show here that a mixed-effects implementation of the propensity adjustment can reduce bias in longitudinal studies of non-equivalent comparison groups. The strategy examined here involves two stages. Initially, a mixed-effects ordinal logistic regression model of propensity for treatment intensity includes variables that differentiate subjects who receive various doses of time-varying treatments. Second, a mixed-effects linear regression model compares the effectiveness of those ordinal doses on a continuous outcome over time. Here, a simulation study compares bias reduction that is achieved by implementing this propensity adjustment through various forms of stratification. The simulations demonstrate that bias decreased monotonically as the number of quantiles used for stratification increased from two to five. This was particularly pronounced with stronger effects of the confounding variables. The quartile and quintile strategies typically removed in excess of 80-90 per cent of the bias detected in unadjusted models; whereas a median-split approach removed from 20 to 45 per cent of bias. The approach is illustrated in an evaluation of the effectiveness of somatic treatments for major depression in a longitudinal, observational study of affective disorders.