Reference based imputation methods integrated with mixed models for addressing intercurrent events

Music City Center 

In the estimand framework, reference-based imputation (RBI) methods are recommended under a hypothetical strategy to indicate unfavorable outcomes for patients with intercurrent events (ICEs). Traditionally RBI methods are used as sensitivity analyses to explore deviations from the missing at random (MAR) assumption. This presentation explores the integration of RBIs with mixed models for repeated measures (MMRMs) in primary analyses for continuous longitudinal endpoints.
Different RBI methods (e.g., jump to reference, copy increments in reference) will be applied to specific ICEs (e.g., death, adverse events) with categorical time MMRMs for analyzing changes at a pre-specified time point or with continuous time MMRMs for analyzing the rate of change over time. Simulation studies will evaluate the operating characteristics of these models. Case studies will demonstrate the application of the proposed RBI methods integrated with MMRMs in real-world scenarios, highlighting strengths and limitations, and clarifying interpretation of results.

reference-based imputation

mixed model for repeated measures

intercurrent events 

Biopharmaceutical Section