Assessing a surrogate predictive value: a causal inference approach.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read More Add to Saved list
  • 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:
      Biomarkers* ; Causality* ; Data Interpretation, Statistical*; Antipsychotic Agents/therapeutic use ; Endpoint Determination ; Haloperidol/therapeutic use ; Humans ; Models, Statistical ; Monte Carlo Method ; Risperidone/therapeutic use ; Schizophrenia/drug therapy
    • Abstract:
      Several methods have been developed for the evaluation of surrogate endpoints within the causal-inference and meta-analytic paradigms. In both paradigms, much effort has been made to assess the capacity of the surrogate to predict the causal treatment effect on the true endpoint. In the present work, the so-called surrogate predictive function (SPF) is introduced for that purpose, using potential outcomes. The relationship between the SPF and the individual causal association, a new metric of surrogacy recently proposed in the literature, is studied in detail. It is shown that the SPF, in conjunction with the individual causal association, can offer an appealing quantification of the surrogate predictive value. However, neither the distribution of the potential outcomes nor the SPF are identifiable from the data. These identifiability issues are tackled using a two-step procedure. In the first step, the region of the parametric space of the distribution of the potential outcomes, compatible with the data at hand, is geometrically characterized. Further, in a second step, a Monte Carlo approach is used to study the behavior of the SPF on the previous region. The method is illustrated using data from a clinical trial involving schizophrenic patients and a newly developed and user friendly R package Surrogate is provided to carry out the validation exercise. Copyright © 2016 John Wiley & Sons, Ltd.
      (Copyright © 2016 John Wiley & Sons, Ltd.)
    • Contributed Indexing:
      Keywords: R package Surrogate; causal inference; sensitivity analysis; surrogate endpoint
    • Accession Number:
      0 (Antipsychotic Agents)
      0 (Biomarkers)
      J6292F8L3D (Haloperidol)
      L6UH7ZF8HC (Risperidone)
    • Publication Date:
      Date Created: 20161215 Date Completed: 20180220 Latest Revision: 20180515
    • Publication Date:
      20231215
    • Accession Number:
      10.1002/sim.7197
    • Accession Number:
      27966231