Evaluating surrogate marker information using censored data.

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  • Author(s): Parast L;Parast L; Cai T; Cai T; Tian L; Tian L
  • Source:
    Statistics in medicine [Stat Med] 2017 May 20; Vol. 36 (11), pp. 1767-1782. Date of Electronic Publication: 2017 Jan 15.
  • Publication Type:
    Journal Article; Research Support, U.S. Gov't, P.H.S.; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • 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:
      Data Interpretation, Statistical* ; Treatment Outcome*; Biomarkers/*analysis; Causality ; Humans ; Models, Statistical ; Probability ; Statistics, Nonparametric
    • Abstract:
      Given the long follow-up periods that are often required for treatment or intervention studies, the potential to use surrogate markers to decrease the required follow-up time is a very attractive goal. However, previous studies have shown that using inadequate markers or making inappropriate assumptions about the relationship between the primary outcome and surrogate marker can lead to inaccurate conclusions regarding the treatment effect. Currently available methods for identifying and validating surrogate markers tend to rely on restrictive model assumptions and/or focus on uncensored outcomes. The ability to use such methods in practice when the primary outcome of interest is a time-to-event outcome is difficult because of censoring and missing surrogate information among those who experience the primary outcome before surrogate marker measurement. In this paper, we propose a novel definition of the proportion of treatment effect explained by surrogate information collected up to a specified time in the setting of a time-to-event primary outcome. Our proposed approach accommodates a setting where individuals may experience the primary outcome before the surrogate marker is measured. We propose a robust non-parametric procedure to estimate the defined quantity using censored data and use a perturbation-resampling procedure for variance estimation. Simulation studies demonstrate that the proposed procedures perform well in finite samples. We illustrate the proposed procedures by investigating two potential surrogate markers for diabetes using data from the Diabetes Prevention Program. Copyright © 2017 John Wiley & Sons, Ltd.
      (Copyright © 2017 John Wiley & Sons, Ltd.)
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    • Grant Information:
      R01 DK118354 United States DK NIDDK NIH HHS; R21 DK103118 United States DK NIDDK NIH HHS; U54 HG007963 United States HG NHGRI NIH HHS; United States CC CDC HHS
    • Contributed Indexing:
      Keywords: non-parametric methods; robust procedures; smoothing; survival analysis
    • Accession Number:
      0 (Biomarkers)
    • Publication Date:
      Date Created: 20170116 Date Completed: 20180308 Latest Revision: 20210508
    • Publication Date:
      20221213
    • Accession Number:
      PMC5413393
    • Accession Number:
      10.1002/sim.7220
    • Accession Number:
      28088843