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Evaluation of Individual and Trial Level Association Metrics in the Validation of a Binary Surrogate Endpoint for a True Time-to-Event Endpoint

Renee Y. Ge, Azadeh Shohoudi, Malini Iyengar, Quefeng Li, Judy Li

Abstract

Candidate binary endpoints are often considered as surrogates for time-to-event (TTE) clinical endpoints, primarily because they can be assessed at earlier time points. To be submitted for regulatory approval candidate binary endpoints need to validated. The most well-known method for performing such validation employs a meta-analytic framework to estimate individual-level and trial-level association. However, the performance of these association estimates in the context of a binary surrogate has not yet been examined through a comprehensive simulation study. This research aims to systematically investigate the performance of association estimates at the trial-level and at the individual-level under various trial design choices, using both simulation studies and clinical trial data, where available.

Evaluation of Individual and Trial Level Association Metrics in the Validation of a Binary Surrogate Endpoint for a True Time-to-Event Endpoint

Abstract

Candidate binary endpoints are often considered as surrogates for time-to-event (TTE) clinical endpoints, primarily because they can be assessed at earlier time points. To be submitted for regulatory approval candidate binary endpoints need to validated. The most well-known method for performing such validation employs a meta-analytic framework to estimate individual-level and trial-level association. However, the performance of these association estimates in the context of a binary surrogate has not yet been examined through a comprehensive simulation study. This research aims to systematically investigate the performance of association estimates at the trial-level and at the individual-level under various trial design choices, using both simulation studies and clinical trial data, where available.
Paper Structure (10 sections, 10 equations, 6 figures, 6 tables)

This paper contains 10 sections, 10 equations, 6 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Surrogacy Evaluation
  3. Simulation Study
  4. Data Generation
  5. Parameter Specification
  6. Simulation Results
  7. Low True Association
  8. High True Association
  9. Clinical Trial Data Application
  10. Discussion

Figures (6)

  • Figure 1: Surrogacy Evaluation Criterion
  • Figure 2: Scatterplots of the global OR against the three trial-level association metrics
  • Figure 3: Forest plot of HR in MM clinical trials
  • Figure 4: Forest plot of OR in MM clinical trials
  • Figure 5: Forest plot of HR for MRD in MM clinical trials
  • ...and 1 more figures