Sensitivity Analysis of Life History Data with Loss to Follow-up: Extending Multistate Models Using Multiple Imputation.

Life history data describes a process that progresses through various stages before reaching a terminal event, providing detailed insights into the entire disease trajectory. It is increasingly used in health research to evaluate treatment effects, risk factors, and policy implementations. However, handling loss to follow-up (LTF) remains a major challenge since conventional multistate models often assume that LTF-induced censoring is independent of the life history process, an assumption that may not hold in practice. This paper addresses this issue through sensitivity analysis, which characterizes deviations from independent censoring and evaluates the impact on treatment effect estimation. We extend the classical multistate model to include separate pre- and post-LTF transition intensities. Using a delta-adjusted (DA) sensitivity assumption, we apply multiple imputation (MI) to generate potential outcomes beyond LTF. The proposed framework is illustrated using real-world data to assess the impact of the World Health Organization's Treat All policy on HIV disease progression.