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Title: Considerations in the evaluation of surrogate endpoints in clinical trials. summary of a National Institutes of Health workshop.
Authors: De Gruttola VG,  Clax P,  DeMets DL,  Downing GJ,  Ellenberg SS,  Friedman L,  Gail MH,  Prentice R,  Wittes J,  Zeger SL
Journal: Control Clin Trials
Date: 2001 Oct
Branches: BB
PubMed ID: 11578783
PMC ID: not available
Abstract: We report on recommendations from a National Institutes of Health Workshop on methods for evaluating the use of surrogate endpoints in clinical trials, which was attended by experts in biostatistics and clinical trials from a broad array of disease areas. Recent advances in biosciences and technology have increased the ability to understand, measure, and model biological mechanisms; appropriate application of these advances in clinical research settings requires collaboration of quantitative and laboratory scientists. Biomarkers, new examples of which arise rapidly from new technologies, are used frequently in such areas as early detection of disease and identification of patients most likely to benefit from new therapies. There is also scientific interest in exploring whether, and under what conditions, biomarkers may substitute for clinical endpoints of phase III trials, although workshop participants agreed that these considerations apply primarily to situations where trials using clinical endpoints are not feasible. Evaluating candidate biomarkers in the exploratory phases of drug development and investigating surrogate endpoints in confirmatory trials require the establishment of a statistical and inferential framework. As a first step, participants reviewed methods for investigating the degree to which biomarkers can explain or predict the effect of treatments on clinical endpoints measured in clinical trials. They also suggested new approaches appropriate in settings where biomarkers reflect only indirectly the important processes on the causal path to clinical disease and where biomarker measurement errors are of concern. Participants emphasized the need for further research on development of such models, whether they are empirical in nature or attempt to describe mechanisms in mathematical terms. Of special interest were meta-analytic models for combining information from multiple studies involving interventions for the same condition. Recommendations also included considerations for design and conduct of trials and for assemblage of databases needed for such research. Finally, there was a strong recommendation for increased training of quantitative scientists in biologic research as well as in statistical methods and modeling to ensure that there will be an adequate workforce to meet future research needs.