08: Statistical Approaches for Sensitivity Analyses Addressing Incomplete Longitudinal, Continuous Data

Music City Center 

There are no standard approaches for analyses of endpoints in randomized controlled trials with incomplete measurements. When data are missing not at random (MNAR), estimates from regular statistical approaches for longitudinal analyses of continuous endpoints (i.e. mixed models for repeated measures or random-coefficient mixed effect models) can be biased. Pattern mixture models (PMMs), joint models (JMs), and multiple imputation (MI) address missing data and can be implemented as ad-hoc sensitivity analyses of the endpoints. The objective of the present study is to compare performance of PMMs, JMs, and MI under different scenarios. Our simulations show that PMMs with the reference-based imputation methods produce biased estimates for parameters of interest and inflate their variances. MI simulations with reference-based imputation methods produce similarly biased estimates with smaller variances. Estimates from JM have similar bias and variance to those from MI but require less model and missingness mechanism assumptions. When applicable, we recommend using JMs over PMMs and MIs for sensitivity analyses.