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

Hongbin Zhang1

  • 1Department of Biostatistics, College of Public Health, University of Kentucky, 725 Rose Street, Lexington, KY 40536.

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|March 27, 2026
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Summary
This summary is machine-generated.

This study introduces a new method to handle loss to follow-up (LTF) in health research. It improves disease progression analysis by accounting for potential biases from patients lost during follow-up.

Keywords:
Treat All policyindependent censoringlife history processlost to follow-upmultiple imputationpeople living with HIVsensitivity analysis

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Life history data analysis is crucial for understanding disease trajectories and treatment effects.
  • Loss to follow-up (LTF) presents a significant challenge in statistical modeling, often violating assumptions of independence.
  • Existing multistate models may yield biased results due to unaddressed LTF.

Purpose of the Study:

  • To develop and validate a novel statistical framework for analyzing life history data with informative loss to follow-up.
  • To assess the impact of deviations from independent censoring on treatment effect estimation.
  • To evaluate the effectiveness of the World Health Organization's Treat All policy on HIV disease progression using the proposed method.

Main Methods:

  • Extension of classical multistate models to incorporate separate pre- and post-LTF transition intensities.
  • Application of delta-adjusted (DA) sensitivity analysis to characterize and quantify the impact of dependent censoring.
  • Utilization of multiple imputation (MI) to generate plausible outcomes for individuals lost to follow-up.

Main Results:

  • The proposed framework provides a robust method for sensitivity analysis in the presence of loss to follow-up.
  • The study quantifies the potential impact of LTF on treatment effect estimates in life history data.
  • The analysis using real-world HIV data demonstrates the practical utility of the approach.

Conclusions:

  • The developed statistical approach effectively addresses the challenge of informative loss to follow-up in life history analysis.
  • Sensitivity analysis is essential for evaluating the robustness of findings when censoring is not independent.
  • The framework offers improved insights into disease progression and policy impact, as exemplified by the HIV Treat All policy evaluation.