Literature DB >> 34874550

Testing for heterogeneity in the utility of a surrogate marker.

Layla Parast1, Tianxi Cai2, Lu Tian3.   

Abstract

In studies that require long-term and/or costly follow-up of participants to evaluate a treatment, there is often interest in identifying and using a surrogate marker to evaluate the treatment effect. While several statistical methods have been proposed to evaluate potential surrogate markers, available methods generally do not account for or address the potential for a surrogate to vary in utility or strength by patient characteristics. Previous work examining surrogate markers has indicated that there may be such heterogeneity, that is, that a surrogate marker may be useful (with respect to capturing the treatment effect on the primary outcome) for some subgroups, but not for others. This heterogeneity is important to understand, particularly if the surrogate is to be used in a future trial to replace the primary outcome. In this paper, we propose an approach and estimation procedures to measure the surrogate strength as a function of a baseline covariate W and thus examine potential heterogeneity in the utility of the surrogate marker with respect to W. Within a potential outcome framework, we quantify the surrogate strength/utility using the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate. We propose testing procedures to test for evidence of heterogeneity, examine finite sample performance of these methods via simulation, and illustrate the methods using AIDS clinical trial data.
© 2021 The International Biometric Society.

Entities:  

Keywords:  heterogeneity; kernel methods; nonparametric methods; potential outcomes; surrogate marker; treatment effect

Year:  2021        PMID: 34874550      PMCID: PMC9170832          DOI: 10.1111/biom.13600

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   1.701


  28 in total

1.  Comparing biomarkers as principal surrogate endpoints.

Authors:  Ying Huang; Peter B Gilbert
Journal:  Biometrics       Date:  2011-04-22       Impact factor: 2.571

2.  Statistical validation of intermediate endpoints for chronic diseases.

Authors:  L S Freedman; B I Graubard; A Schatzkin
Journal:  Stat Med       Date:  1992-01-30       Impact factor: 2.373

3.  Related causal frameworks for surrogate outcomes.

Authors:  Marshall M Joffe; Tom Greene
Journal:  Biometrics       Date:  2009-06       Impact factor: 2.571

4.  Evaluating candidate principal surrogate endpoints.

Authors:  Peter B Gilbert; Michael G Hudgens
Journal:  Biometrics       Date:  2008-03-24       Impact factor: 2.571

5.  Meta-analysis for the evaluation of potential surrogate markers.

Authors:  M J Daniels; M D Hughes
Journal:  Stat Med       Date:  1997-09-15       Impact factor: 2.373

6.  Estimating the proportion of treatment effect explained by a surrogate marker.

Authors:  D Y Lin; T R Fleming; V De Gruttola
Journal:  Stat Med       Date:  1997-07-15       Impact factor: 2.373

7.  A method to address between-subject heterogeneity for identification of principal surrogate markers in repeated low-dose challenge HIV vaccine studies.

Authors:  Andrew J Spieker; Ying Huang
Journal:  Stat Med       Date:  2017-07-31       Impact factor: 2.373

8.  A controlled trial of two nucleoside analogues plus indinavir in persons with human immunodeficiency virus infection and CD4 cell counts of 200 per cubic millimeter or less. AIDS Clinical Trials Group 320 Study Team.

Authors:  S M Hammer; K E Squires; M D Hughes; J M Grimes; L M Demeter; J S Currier; J J Eron; J E Feinberg; H H Balfour; L R Deyton; J A Chodakewitz; M A Fischl
Journal:  N Engl J Med       Date:  1997-09-11       Impact factor: 91.245

9.  Early Change in Urine Protein as a Surrogate End Point in Studies of IgA Nephropathy: An Individual-Patient Meta-analysis.

Authors:  Lesley A Inker; Hasi Mondal; Tom Greene; Taylor Masaschi; Francesco Locatelli; Francesco P Schena; Ritsuko Katafuchi; Gerald B Appel; Bart D Maes; Philip K Li; Manuel Praga; Lucia Del Vecchio; Simeone Andrulli; Carlo Manno; Eduardo Gutierrez; Alex Mercer; Kevin J Carroll; Christopher H Schmid; Andrew S Levey
Journal:  Am J Kidney Dis       Date:  2016-03-29       Impact factor: 8.860

Review 10.  From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer.

Authors:  Richard J Willke; Zhiyuan Zheng; Prasun Subedi; Rikard Althin; C Daniel Mullins
Journal:  BMC Med Res Methodol       Date:  2012-12-13       Impact factor: 4.615

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