Pharm Stat. 2026 Mar-Apr;25(2):e70070. doi: 10.1002/pst.70070.
ABSTRACT
The estimand framework proposes different strategies to address intercurrent events. The treatment policy strategy seems to be the most favoured as it is closely aligned with the pre-addendum intention-to-treat principle. All data for all patients should ideally be collected; however, in reality patients may withdraw from a study leading to missing data. This needs to be dealt with as part of the estimation. A common intercurrent event we focus on is treatment discontinuation. Several areas of research have been conducted exploring models to estimate the estimand when intercurrent events are handled using a treatment policy strategy; however, the research is limited for binary endpoints. We explore different retrieved dropout models, where post-intercurrent event, the observed data can be used to multiply impute the missing post-intercurrent event data. We compare our proposed models to a simple imputation model that makes no distinction between the pre- and post-intercurrent event data, and assess varying statistical properties through a simulation study. We then provide an example of how retrieved dropout models were used in practice for Phase 3 clinical trials in rheumatoid arthritis. From the models explored, we conclude that a simple retrieved dropout model including an indicator for whether or not the intercurrent event occurred is the most pragmatic choice. However, at least 50% of observed post-intercurrent event data is required for these models to work well. Therefore, the suitability of implementing this model in practice will depend on the amount of observed post-intercurrent event data available and missing data.
PMID:41652801 | DOI:10.1002/pst.70070
