Literature DB >> 32506496

Evaluation of longitudinal surrogate markers.

Denis Agniel1, Layla Parast1.   

Abstract

The use of surrogate markers to examine the effectiveness of a treatment has the potential to decrease study length and identify effective treatments more quickly. Most available methods to investigate the usefulness of a surrogate marker involve restrictive parametric assumptions and tend to focus on settings where the surrogate is measured at a single point in time. However, in many clinical settings, the potential surrogate marker is often measured repeatedly over time, and thus, the surrogate marker information is a trajectory of measurements. In addition, it is often difficult in practice to correctly specify the relationship between a treatment, primary outcome, and surrogate marker trajectory. In this paper, we propose a model-free definition for the proportion of the treatment effect on the primary outcome that is explained by the treatment effect on the longitudinal surrogate markers. We propose three novel flexible methods to estimate this proportion, develop the asymptotic properties of our estimators, and investigate the robustness of the estimators under multiple settings via a simulation study. We apply our proposed procedures to an AIDS clinical trial dataset to examine a trajectory of CD4 counts as a potential surrogate.
© 2020 The International Biometric Society.

Entities:  

Keywords:  functional data; kernel smoothing; longitudinal data; nonparametric analysis; surrogate markers

Year:  2020        PMID: 32506496      PMCID: PMC8015060          DOI: 10.1111/biom.13310

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


  19 in total

1.  A measure of the proportion of treatment effect explained by a surrogate marker.

Authors:  Yue Wang; Jeremy M G Taylor
Journal:  Biometrics       Date:  2002-12       Impact factor: 2.571

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.  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

4.  Statistical validation of intermediate endpoints for chronic diseases.

Authors:  L S Freedman; B I Graubard; A Schatzkin
Journal:  Stat Med       Date:  1992-01-30       Impact factor: 2.373

5.  Related causal frameworks for surrogate outcomes.

Authors:  Marshall M Joffe; Tom Greene
Journal:  Biometrics       Date:  2009-06       Impact factor: 2.571

6.  Evaluating candidate principal surrogate endpoints.

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

7.  Penalized Functional Regression.

Authors:  Jeff Goldsmith; Jennifer Bobb; Ciprian M Crainiceanu; Brian Caffo; Daniel Reich
Journal:  J Comput Graph Stat       Date:  2011-12-01       Impact factor: 2.302

8.  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

9.  Functional Generalized Additive Models.

Authors:  Mathew W McLean; Giles Hooker; Ana-Maria Staicu; Fabian Scheipl; David Ruppert
Journal:  J Comput Graph Stat       Date:  2014       Impact factor: 2.302

10.  Early Change in Urine Protein as a Surrogate End Point in Studies of IgA Nephropathy: An Individual-Patient Meta-analysis.

Authors:  Lesley A Inker; Hasi Mondal; Tom Greene; Taylor Masaschi; Francesco Locatelli; Francesco P Schena; Ritsuko Katafuchi; Gerald B Appel; Bart D Maes; Philip K Li; Manuel Praga; Lucia Del Vecchio; Simeone Andrulli; Carlo Manno; Eduardo Gutierrez; Alex Mercer; Kevin J Carroll; Christopher H Schmid; Andrew S Levey
Journal:  Am J Kidney Dis       Date:  2016-03-29       Impact factor: 8.860

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.