The Choice of Analytical Strategies in Inverse-Probability-of-Treatment–Weighted Analysis: A Simulation Study

We sought to explore the impact of intention to treat and complex treatment use assumptions made during weight construction on the validity and precision of estimates derived from inverse-probability-of-treatment-weighted analysis. We simulated data assuming a nonexperimental design that attempted to quantify the effect of statin on lowering low-density lipoprotein cholesterol. We created 324 scenarios by varying parameter values (effect size, sample size, adherence level, probability of treatment initiation, associations between low-density lipoprotein cholesterol and treatment initiation and continuation). Four analytical approaches were used: 1) assuming intention to treat; 2) assuming complex mechanisms of treatment use; 3) assuming a simple mechanism of treatment use; and 4) assuming invariant confounders. With a continuous outcome, estimates assuming intention to treat were biased toward the null when there were nonnull treatment effect and nonadherence after treatment initiation. For each 1% decrease in the proportion of patients staying on treatment after initiation, the bias in estimated average treatment effect increased by 1%. Inverse-probability-of-treatment-weighted analyses that took into account the complex mechanisms of treatment use generated approximately unbiased estimates. Studies estimating the actual effect of a time-varying treatment need to consider the complex mechanisms of treatment use during weight construction.