Literature DB >> 24073861

Surrogate measures and consistent surrogates.

Tyler J Vanderweele1.   

Abstract

Surrogates which allow one to predict the effect of the treatment on the outcome of interest from the effect of the treatment on the surrogate are of importance when it is difficult or expensive to measure the primary outcome. Unfortunately, the use of such surrogates can give rise to paradoxical situations in which the effect of the treatment on the surrogate is positive, the surrogate and outcome are strongly positively correlated, but the effect of the treatment on the outcome is negative, a phenomenon sometimes referred to as the "surrogate paradox." New results are given for consistent surrogates that extend the existing literature on sufficient conditions that ensure the surrogate paradox is not manifest. Specifically, it is shown that for the surrogate paradox to be manifest it must be the case that either there is (i) a direct effect of treatment on the outcome not through the surrogate and in the opposite direction as that through the surrogate or (ii) confounding for the effect of the surrogate on the outcome, or (iii) a lack of transitivity so that treatment does not positively affect the surrogate for all the same individuals for whom the surrogate positively affects the outcome. The conditions for consistent surrogates and the results of the article are important because they allow investigators to predict the direction of the effect of the treatment on the outcome simply from the direction of the effect of the treatment on the surrogate. These results on consistent surrogates are then related to the four approaches to surrogate outcomes described by Joffe and Greene (2009, Biometrics 65, 530-538) to assess whether the standard criteria used by these approaches to assess whether a surrogate is "good" suffice to avoid the surrogate paradox.
© 2013, The International Biometric Society.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 24073861      PMCID: PMC4221255          DOI: 10.1111/biom.12071

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  21 in total

1.  Principal stratification in causal inference.

Authors:  Constantine E Frangakis; Donald B Rubin
Journal:  Biometrics       Date:  2002-03       Impact factor: 2.571

2.  Comparing biomarkers as principal surrogate endpoints.

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

3.  On meta-analytic assessment of surrogate outcomes.

Authors:  M H Gail; R Pfeiffer; H C Van Houwelingen; R J Carroll
Journal:  Biostatistics       Date:  2000-09       Impact factor: 5.899

4.  A general approach to causal mediation analysis.

Authors:  Kosuke Imai; Luke Keele; Dustin Tingley
Journal:  Psychol Methods       Date:  2010-12

5.  Identifiability and exchangeability for direct and indirect effects.

Authors:  J M Robins; S Greenland
Journal:  Epidemiology       Date:  1992-03       Impact factor: 4.822

6.  Counterfactual links to the proportion of treatment effect explained by a surrogate marker.

Authors:  Jeremy M G Taylor; Yue Wang; Rodolphe Thiébaut
Journal:  Biometrics       Date:  2005-12       Impact factor: 2.571

7.  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

8.  Surrogate endpoints in clinical trials: definition and operational criteria.

Authors:  R L Prentice
Journal:  Stat Med       Date:  1989-04       Impact factor: 2.373

9.  Causal directed acyclic graphs and the direction of unmeasured confounding bias.

Authors:  Tyler J VanderWeele; Miguel A Hernán; James M Robins
Journal:  Epidemiology       Date:  2008-09       Impact factor: 4.822

10.  Statistical identifiability and the surrogate endpoint problem, with application to vaccine trials.

Authors:  Julian Wolfson; Peter Gilbert
Journal:  Biometrics       Date:  2010-12       Impact factor: 2.571

View more
  28 in total

1.  Augmented trial designs for evaluation of principal surrogates.

Authors:  Erin E Gabriel; Dean Follmann
Journal:  Biostatistics       Date:  2016-01-28       Impact factor: 5.899

2.  Evaluating surrogate marker information using censored data.

Authors:  Layla Parast; Tianxi Cai; Lu Tian
Journal:  Stat Med       Date:  2017-01-15       Impact factor: 2.373

3.  Surrogacy assessment using principal stratification when surrogate and outcome measures are multivariate normal.

Authors:  Anna S C Conlon; Jeremy M G Taylor; Michael R Elliott
Journal:  Biostatistics       Date:  2013-11-26       Impact factor: 5.899

4.  Surrogacy marker paradox measures in meta-analytic settings.

Authors:  Michael R Elliott; Anna S C Conlon; Yun Li; Nico Kaciroti; Jeremy M G Taylor
Journal:  Biostatistics       Date:  2014-09-17       Impact factor: 5.899

5.  Evaluation of longitudinal surrogate markers.

Authors:  Denis Agniel; Layla Parast
Journal:  Biometrics       Date:  2020-06-22       Impact factor: 2.571

Review 6.  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

7.  Surrogate markers for time-varying treatments and outcomes.

Authors:  Jesse Y Hsu; Edward H Kennedy; Jason A Roy; Alisa J Stephens-Shields; Dylan S Small; Marshall M Joffe
Journal:  Clin Trials       Date:  2015-05-06       Impact factor: 2.486

8.  Progression-Free Survival as a Surrogate for Overall Survival in Clinical Trials of Targeted Therapy in Advanced Solid Tumors.

Authors:  Stefan Michiels; Everardo D Saad; Marc Buyse
Journal:  Drugs       Date:  2017-05       Impact factor: 9.546

9.  Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition.

Authors:  Peter B Gilbert; Erin E Gabriel; Ying Huang; Ivan S F Chan
Journal:  J Causal Inference       Date:  2015-02-01

10.  Assessing the value of a censored surrogate outcome.

Authors:  Layla Parast; Lu Tian; Tianxi Cai
Journal:  Lifetime Data Anal       Date:  2019-04-12       Impact factor: 1.588

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.