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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Further Practical Guidance on Adjusting Time-To-Event Outcomes for Treatment Switching.

Claire Watkins¹, Eva Kleine², Miguel Miranda³

¹Clarostat Consulting Ltd., Bollington, UK.

Pharmaceutical Statistics

|June 21, 2025

Summary

This paper offers practical guidance for statistical analyses that adjust for treatment switching in long-term outcomes. It details methods like rank preserving structural failure time models (RPSFTM) to estimate treatment effects without switching.

Keywords:

inverse probability of censoring weighting rank preserving structural failure time recensoring survival analysis treatment switching two stage

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Area of Science:

Biostatistics
Clinical Trial Analysis
Epidemiology

Background:

Treatment switching in clinical trials complicates the estimation of long-term treatment effects.
Patients often switch therapies, introducing confounding that biases observed outcomes like overall survival.

Purpose of the Study:

To consolidate current information and practical considerations for statistical analyses adjusting for treatment switching.
To provide a comprehensive reference for analysts, incorporating practical experience and best practices.

Main Methods:

Discusses three primary complex statistical approaches: rank preserving structural failure time models (RPSFTM), two-stage estimation (TSE), and inverse probability of censoring weighting (IPCW).
Explains theoretical underpinnings, practical application guidance, and summarizes extensions and alternatives.
Addresses key concepts like recensoring and sources of variability.

Main Results:

Provides an overview of available software and programming guidance, including an R code repository.
Reviews the acceptability of these methods by regulatory and health technology assessment agencies.
Highlights potential pitfalls and offers recommendations for best practice in analysis and reporting.

Conclusions:

This paper serves as a single-source reference, saving statisticians time and effort by consolidating scattered guidance on adjusting for treatment switching.
Aims to enhance the quality of statistical analyses and reporting for time-to-event outcomes affected by treatment switching.