Considerations for choosing an imputation method for addressing sparse measurement issues dictated by the study design - An illustration from per-protocol analysis in pragmatic trials

Abstract

Last Observation Carried Forward (LOCF) is an ad-hoc method, with known limitations. In recent years, several methods publications have used LOCF in estimating the per-protocol effect via inverse probability of adherence weighted (IPAW) model, when a time-varying factor is partially measured by the study design. We compare the statistical performances of LOCF and multiple imputation approaches for estimating the per-protocol effects via the IPAW model in the presence of incomplete treatment adherence. We used a validated pragmatic trial data generating simulation algorithm to generate datasets under 7 different simulation scenarios, where a post-randomization prognostic factor was measured after regular intervals. Unmeasured values of a partially observed factor were imputed using LOCF and multiple imputation approaches, and IPAW model was fitted on the imputed data to obtain the estimates, and statistical performances were assessed. When confounding exists, for higher variability of the time-varying factor, multiple imputation approach shows desirable statistical properties under MCAR assumption; otherwise, LOCF approach can be adequate. Both imputation methods performed well in terms of statistical properties, when there is no confounding or when all necessary confounders are adjusted. A case study from Coronary Primary Prevention Trial data was presented, which included some participants with incomplete treatment adherence.