Comparing and combining biomarkers as principal surrogates for time-to-event clinical endpoints.

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  • Author(s): Gabriel EE;Gabriel EE; Sachs MC; Gilbert PB
  • Source:
    Statistics in medicine [Stat Med] 2015 Feb 10; Vol. 34 (3), pp. 381-95. Date of Electronic Publication: 2014 Oct 28.
  • Publication Type:
    Comparative Study; Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
    • Publication Information:
      Original Publication: Chichester ; New York : Wiley, c1982-
    • Subject Terms:
      Multivariate Analysis*; Biomarkers/*analysis ; Randomized Controlled Trials as Topic/*methods; Causality ; Clinical Trials, Phase I as Topic ; Clinical Trials, Phase II as Topic ; Computer Simulation ; Diabetes Mellitus, Type 1/drug therapy ; Diabetic Nephropathies ; Glomerular Filtration Rate ; Glycated Hemoglobin ; Humans ; Likelihood Functions ; Risk Factors ; Time Factors ; Treatment Outcome
    • Abstract:
      Principal surrogate endpoints are useful as targets for phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates, and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain, and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial.
      (Copyright © 2014 John Wiley & Sons, Ltd.)
    • Comments:
      Erratum in: Stat Med. 2017 Sep 20;36(21):3440. (PMID: 28776727)
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    • Grant Information:
      R37 AI054165 United States AI NIAID NIH HHS; UM1 AI068635 United States AI NIAID NIH HHS
    • Contributed Indexing:
      Keywords: accuracy measures; causal inference; multivariate principal stratification; surrogate endpoint evaluation; survival analysis
    • Accession Number:
      0 (Biomarkers)
      0 (Glycated Hemoglobin A)
      0 (hemoglobin A1c protein, human)
    • Publication Date:
      Date Created: 20141030 Date Completed: 20150924 Latest Revision: 20221207
    • Publication Date:
      20240628
    • Accession Number:
      PMC4801510
    • Accession Number:
      10.1002/sim.6349
    • Accession Number:
      25352131