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Reference-based sensitivity analysis for time-to-event data.

Andrew Atkinson1,2, Michael G Kenward3, Tim Clayton1

  • 1Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.

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|July 17, 2019
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Summary
This summary is machine-generated.

This study introduces reference-based imputation for time-to-event data, enhancing robustness in survival analysis when patients deviate from assigned treatments after censoring.

Keywords:
MNARmissing datamultiple imputationsensitivity analysistime to event

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Survival Analysis

Background:

  • Standard time-to-event analysis assumes censoring is random, aligning with 'on-treatment' strategies.
  • Pragmatic analyses require assessing robustness to 'treatment policy' assumptions, especially when patients switch care post-censoring.

Purpose of the Study:

  • To propose reference-based assumptions for time-to-event data in survival analysis.
  • To evaluate the information anchoring of multiple imputation in this context.
  • To demonstrate the method using a real-world randomized trial.

Main Methods:

  • Developing reference-based imputation assumptions for survival data.
  • Conducting a simulation study to assess the information anchoring of the multiple imputation estimator (using Rubin's variance formula).
  • Reanalyzing data from a randomized trial comparing medical therapy and angioplasty for angina.

Main Results:

  • The proposed reference-based imputation class is suitable for time-to-event data.
  • The multiple imputation estimator demonstrates approximate information anchoring in the survival context.
  • The reanalysis provides insights into treatment effects under pragmatic assumptions.

Conclusions:

  • Reference-based imputation offers a robust approach for sensitivity analyses in time-to-event studies.
  • This method enhances the interpretation of clinical trial results by accounting for post-censoring treatment behavior.
  • The approach is practical and aids in understanding real-world treatment effectiveness.