Literature DB >> 9203643

Perspective: validating surrogate markers--are we being naive?

V De Gruttola1, T Fleming, D Y Lin, R Coombs.   

Abstract

Because of the difficulties in conducting studies of clinical efficacy of new therapies for human immunodeficiency virus infection and other diseases, there is increasing interest in using measures of biologic activity as surrogates for clinical end points. A widely used criterion for evaluating whether such measures are reliable as surrogates requires that the putative surrogate fully captures the "net effect"-the effect aggregated over all mechanisms of action-of the treatment on the clinical end point. The variety of proposed metrics for evaluating the degree to which this criterion is met are subject to misinterpretation because of the multiplicity of mechanisms by which drugs operate. Without detailed understanding of these mechanisms, metrics of "surrogacy" are not directly interpretable. Even when all of the mechanisms are understood, these metrics are associated with a high degree of uncertainty unless either treatment effects are large in moderate-size studies or sample sizes are large in studies of moderately effective treatments.

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Year:  1997        PMID: 9203643     DOI: 10.1093/infdis/175.2.237

Source DB:  PubMed          Journal:  J Infect Dis        ISSN: 0022-1899            Impact factor:   5.226


  21 in total

1.  An information-theoretic approach to surrogate-marker evaluation with failure time endpoints.

Authors:  Assam Pryseley; Abel Tilahun; Ariel Alonso; Geert Molenberghs
Journal:  Lifetime Data Anal       Date:  2010-09-28       Impact factor: 1.588

Review 2.  What are biomarkers?

Authors:  Kyle Strimbu; Jorge A Tavel
Journal:  Curr Opin HIV AIDS       Date:  2010-11       Impact factor: 4.283

3.  Evaluating the Proportion of Treatment Effect Explained by a Continuous Surrogate Marker in Logistic or Probit Regression Models.

Authors:  Jie Huang; Bin Huang
Journal:  Stat Biopharm Res       Date:  2010-05-01       Impact factor: 1.452

4.  Predicting treatment effects using biomarker data in a meta-analysis of clinical trials.

Authors:  Y Li; J M G Taylor
Journal:  Stat Med       Date:  2010-08-15       Impact factor: 2.373

Review 5.  Distinguishing Causation From Correlation in the Use of Correlates of Protection to Evaluate and Develop Influenza Vaccines.

Authors:  Wey Wen Lim; Nancy H L Leung; Sheena G Sullivan; Eric J Tchetgen Tchetgen; Benjamin J Cowling
Journal:  Am J Epidemiol       Date:  2020-03-02       Impact factor: 4.897

6.  Biomarkers and surrogate endpoints in kidney disease.

Authors:  Erum A Hartung
Journal:  Pediatr Nephrol       Date:  2015-05-16       Impact factor: 3.714

7.  Progression-free survival as a surrogate endpoint in advanced breast cancer.

Authors:  Rebecca A Miksad; Vera Zietemann; Raffaella Gothe; Ruth Schwarzer; Annette Conrads-Frank; Petra Schnell-Inderst; Björn Stollenwerk; Uwe Siebert
Journal:  Int J Technol Assess Health Care       Date:  2008       Impact factor: 2.188

Review 8.  Surrogate endpoints in Parkinson's disease research.

Authors:  Kevin M Biglan; Robert G Holloway
Journal:  Curr Neurol Neurosci Rep       Date:  2003-07       Impact factor: 5.081

9.  A Brief Chronicle of CD4 as a Biomarker for HIV/AIDS: A Tribute to the Memory of John L. Fahey.

Authors:  Jonathan M Kagan; Ana M Sanchez; Alan Landay; Thomas N Denny
Journal:  For Immunopathol Dis Therap       Date:  2015

Review 10.  Meta-analysis for the evaluation of surrogate endpoints in cancer clinical trials.

Authors:  Qian Shi; Daniel J Sargent
Journal:  Int J Clin Oncol       Date:  2009-04-24       Impact factor: 3.402

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