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Evaluating surrogate marker information using censored data.

Layla Parast1, Tianxi Cai2, Lu Tian3

  • 1RAND Corporation, 1776 Main Street, Santa Monica, 90401, CA, U.S.A.

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|January 16, 2017
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Summary
This summary is machine-generated.

This study introduces a new method to assess how well surrogate markers predict treatment effects in long-term studies, especially for time-to-event outcomes. The approach handles missing data and censoring, improving the reliability of surrogate marker validation.

Keywords:
non-parametric methodsrobust proceduressmoothingsurvival analysis

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Surrogate markers can shorten lengthy clinical trial follow-up periods.
  • Existing methods for validating surrogate markers have limitations, particularly with time-to-event outcomes and censored data.

Purpose of the Study:

  • To propose a novel definition for the proportion of treatment effect explained by surrogate markers for time-to-event outcomes.
  • To develop a robust statistical procedure for estimating this quantity in the presence of censoring and missing surrogate data.

Main Methods:

  • A non-parametric estimation procedure is proposed for censored time-to-event data.
  • A perturbation-resampling method is used for variance estimation.
  • The approach accommodates scenarios where the primary outcome occurs before surrogate marker measurement.

Main Results:

  • Simulation studies indicate the proposed procedures perform well in finite samples.
  • The method is illustrated using data from the Diabetes Prevention Program to evaluate potential surrogate markers for diabetes.

Conclusions:

  • The novel approach provides a valid method for assessing surrogate marker utility in time-to-event studies.
  • This methodology enhances the reliability of surrogate marker validation, even with complex data structures.