Literature DB >> 16220497

A unifying approach for surrogate marker validation based on Prentice's criteria.

Ariel Alonso1, Geert Molenberghs, Helena Geys, Marc Buyse, Tony Vangeneugden.   

Abstract

Part of the recent literature on the evaluation of surrogate endpoints starts from a multi-trial approach which leads to a definition of validity in terms of the quality of both trial-level and individual-level association between a potential surrogate and a true endpoint, Buyse et al. These authors proposed their methodology based on the simplest cross-sectional case in which both the surrogate and the true endpoint are continuous and normally distributed. Different variations to this theme have been implemented for binary responses, times to event, combinations of binary and continuous endpoints, etc. However, a drawback of this methodology is that different settings have led to different definitions to quantify the association at the individual-level. In the longitudinal setting; Alonso et al. defined a class of canonical correlation functions that can be used to study surrogacy at the trial and individual-level. In the present work, we propose a new approach to evaluate surrogacy in the repeated measurements framework, we also show the connection between this proposal and the previous ones reported in the literature. Finally, we extend this concept to the non-normal case using the so-called 'likelihood reduction factor' (LRF) a new validation measure based on some of the Prentice's criteria. We apply the previous methodology using data from two clinical studies in psychiatry and ophthalmology. Copyright (c) 2005 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Substances:

Year:  2006        PMID: 16220497     DOI: 10.1002/sim.2315

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  13 in total

1.  A unified procedure for meta-analytic evaluation of surrogate end points in randomized clinical trials.

Authors:  James Y Dai; James P Hughes
Journal:  Biostatistics       Date:  2012-03-06       Impact factor: 5.899

Review 2.  Biomarkers and surrogate end points--the challenge of statistical validation.

Authors:  Marc Buyse; Daniel J Sargent; Axel Grothey; Alastair Matheson; Aimery de Gramont
Journal:  Nat Rev Clin Oncol       Date:  2010-04-06       Impact factor: 66.675

3.  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

4.  Considerations for development of surrogate endpoints for antifracture efficacy of new treatments in osteoporosis: a perspective.

Authors:  Mary L Bouxsein; Pierre D Delmas
Journal:  J Bone Miner Res       Date:  2008-08       Impact factor: 6.741

5.  Center-Within-Trial Versus Trial-Level Evaluation of Surrogate Endpoints.

Authors:  Lindsay A Renfro; Qian Shi; Yuan Xue; Junlong Li; Hongwei Shang; Daniel J Sargent
Journal:  Comput Stat Data Anal       Date:  2014-10-01       Impact factor: 1.681

Review 6.  Surrogate endpoints in liver surgery related trials: a systematic review of the literature.

Authors:  Liliane Mpabanzi; Kim M C van Mierlo; Massimo Malagó; Cornelis H C Dejong; Dimitrios Lytras; Steven W M Olde Damink
Journal:  HPB (Oxford)       Date:  2012-10-22       Impact factor: 3.647

Review 7.  Chipping away at diagnostics for neurodegenerative diseases.

Authors:  Clemens R Scherzer
Journal:  Neurobiol Dis       Date:  2009-03-10       Impact factor: 5.996

Review 8.  The validity of biomarkers as surrogate endpoints in Alzheimer's disease by means of the Quantitative Surrogate Validation Level of Evidence Scheme (QSVLES).

Authors:  C C Gispen-de Wied; M Kritsidima; A J A Elferink
Journal:  J Nutr Health Aging       Date:  2009-04       Impact factor: 4.075

9.  Measuring Surrogacy in Clinical Research: With an application to studying surrogate markers for HIV Treatment-as-Prevention.

Authors:  Rui Zhuang; Ying Qing Chen
Journal:  Stat Biosci       Date:  2019-06-04

10.  Imaging response assessment in oncology.

Authors:  S D Curran; A U Muellner; L H Schwartz
Journal:  Cancer Imaging       Date:  2006-10-31       Impact factor: 3.909

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.