Literature DB >> 29354294

An Introduction to Principal Surrogate Evaluation with the pseval Package.

Michael C Sachs1, Erin E Gabriel2.   

Abstract

We describe a new package called pseval that implements the core methods for the evaluation of principal surrogates in a single clinical trial. It provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation methods are provided, including print, summary, plot, and testing. We summarize the main statistical methods that are implemented in the package and illustrate its use from the perspective of a novice R user.

Entities:  

Year:  2016        PMID: 29354294      PMCID: PMC5774631     

Source DB:  PubMed          Journal:  R J        ISSN: 2073-4859            Impact factor:   3.984


  10 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.  Nomenclature for immune correlates of protection after vaccination.

Authors:  Stanley A Plotkin; Peter B Gilbert
Journal:  Clin Infect Dis       Date:  2012-03-20       Impact factor: 9.079

4.  Augmented trial designs for evaluation of principal surrogates.

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

5.  Augmented designs to assess immune response in vaccine trials.

Authors:  Dean Follmann
Journal:  Biometrics       Date:  2006-12       Impact factor: 2.571

6.  Evaluating principal surrogate endpoints with time-to-event data accounting for time-varying treatment efficacy.

Authors:  Erin E Gabriel; Peter B Gilbert
Journal:  Biostatistics       Date:  2013-12-13       Impact factor: 5.899

7.  Evaluating candidate principal surrogate endpoints.

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

8.  Comparing and combining biomarkers as principal surrogates for time-to-event clinical endpoints.

Authors:  Erin E Gabriel; Michael C Sachs; Peter B Gilbert
Journal:  Stat Med       Date:  2014-10-28       Impact factor: 2.373

9.  Design and estimation for evaluating principal surrogate markers in vaccine trials.

Authors:  Ying Huang; Peter B Gilbert; Julian Wolfson
Journal:  Biometrics       Date:  2013-02-14       Impact factor: 2.571

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

  10 in total
  1 in total

1.  Evaluation and comparison of predictive individual-level general surrogates.

Authors:  Erin E Gabriel; Michael C Sachs; M Elizabeth Halloran
Journal:  Biostatistics       Date:  2018-07-01       Impact factor: 5.899

  1 in total

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