Sensitivity Analysis of Lost to Follow-up in Life History Data with Multiple Imputation

Music City Center 

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 trace data informed sensitivity assumption, we apply a model-based multiple imputation (MI) to generate the entire latent transitions before and after LTF. We assess the performance of our method through simulation and apply the method using real-world data to assess the impact of the World Health Organization's Treat All policy on HIV disease progression.

life history data

multistate model

lost to follow-up

multiple imputation.

sensitivity analysis 

International Chinese Statistical Association