UBC Theses and Dissertations
Exploring inverse probability weighted per-protocol estimates to adjust for non-adherence using post-randomization covariates : a simulation study Mosquera, Lucy
In pragmatic trials, treatment strategies are randomly assigned at baseline, but patients may not adhere to their assigned treatments during follow-up. In the presence of non-adherence, we aim to compare the conventionally used analyses (e.g. intention-to-treat (ITT) and naive per-protocol (PP)) with inverse probability weighted (IPW) and baseline adjusted PP analyses. We have conducted comprehensive simulation studies to generate realistic two-armed pragmatic trial data with a baseline covariate and post-randomization time-varying covariates. Our simulation was applied to understand the impact of trial characteristics (e.g., nonadherence rates, event rates, trial size), varying the causal relationships (e.g., if the baseline covariate is unmeasured or a risk factor), and varying the measurement schedule for adherence rates and time-varying covariates in the follow-up period. We also assessed the key statistical properties of these estimators. In the presence of non-adherence, our results suggest that ITT, IPW-PP and baseline adjusted PP estimates can recover the true null treatment effect. For non-null treatment effects, only the IPW-PP and baseline adjusted estimates were reasonably unbiased. If adherence and time-varying covariates are assessed less frequently, the bias and variability of effect estimates increase. This study demonstrates the feasability of using adjusted PP estimates to recover the true effect of treatment in the presence of non-adherence and the necessity of designing pragmatic trials that measure both pre-and-post-randomization covariates to reduce bias in the estimation of the treatment effect.
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