Handling Missing Outcomes in Time-to-Event Analyses: A Scoping Review of Multiple Imputation in Randomised Controlled Trials

Background Randomized Controlled Trials (RCTs) are the gold standard for evaluating treatment effects. However, several factors can threaten the validity of findings, including missing outcomes. Missing data pose a unique challenge in time-to-event analyses, where the event time may be censored rather than completely missing. Proper handling of missing event times is crucial to ensure unbiased and reliable conclusions in RCTs. This scoping review examines how missing outcomes in time-to-event studies have been addressed in high-impact medical journals and evaluates the implementation and reporting of multiple imputation (MI) techniques in RCTs. Method This scoping review assessed methods for handling missing time-to-event outcomes in RCTs published between 2019 and 2023 in three high-impact medical journals: The New England Journal of Medicine, The Lancet, and The Journal of the American Medical Association. Studies with time-to-event as the primary outcome were included. If missing outcomes were present, a full review was conducted to assess the methods used and how they were reported, including details on multiple imputation (MI). The review also explored theoretical approaches for imputing censored event times. Results A total of 694 articles were identified through a PubMed search. After screening, 321 RCTs underwent full-text review. Of these, 297 (92.2%) had no or < 10% missing outcomes without imputation. The remaining 17 (5.3%) addressed missing data using statistical methods: 10 used MI, 6 used best-/worst-case scenarios, and 1 applied a propensity score method. MI approaches varied, with some studies lacking detailed reporting. Conclusion In RCTs with survival outcomes, properly handling missing event times is essential. This scoping review reveals that, despite the availability of robust statistical methods, the treatment of missing time-to-event outcomes remains underutilized and often poorly documented. Many studies acknowledge censoring but fail to distinguish between informative and non-informative censoring. Additionally, the reporting of multiple imputation techniques is frequently insufficient. These findings highlight a critical gap in the handling and reporting of missing outcomes in survival analysis. Strengthening these practices will enhance the reliability and reproducibility of survival analyses in RCTs.