Literature DB >> 12933622

Identification and efficacy of longitudinal markers for survival.

Robin Henderson1, Peter Diggle, Angela Dobson.   

Abstract

Methods for the combined analysis of survival time and longitudinal biomarker data have been developed in recent years, with most emphasis on modelling and estimation. This paper focuses on the use of longitudinal marker trajectories as individual-level surrogates for survival. A score test for association which requires only standard methods for implementation is derived for the initial identification of candidate biomarkers. Methods for assessing efficacy of markers are discussed and a measure contrasting conditional and marginal distributions is proposed. An application using prothrombin index as biomarker for survival of liver cirrhosis patients is included.

Entities:  

Year:  2002        PMID: 12933622     DOI: 10.1093/biostatistics/3.1.33

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  14 in total

1.  Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo-observation approach.

Authors:  Lili Zhao; Susan Murray; Laura H Mariani; Wenjun Ju
Journal:  Stat Med       Date:  2020-07-27       Impact factor: 2.373

2.  Predictive accuracy of covariates for event times.

Authors:  Li Chen; D Y Lin; Donglin Zeng
Journal:  Biometrika       Date:  2012-04-29       Impact factor: 2.445

3.  Penalised logistic regression and dynamic prediction for discrete-time recurrent event data.

Authors:  Entisar Elgmati; Rosemeire L Fiaccone; R Henderson; John N S Matthews
Journal:  Lifetime Data Anal       Date:  2015-01-28       Impact factor: 1.588

4.  Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: Development and validation.

Authors:  Mbéry Sène; Jeremy Mg Taylor; James J Dignam; Hélène Jacqmin-Gadda; Cécile Proust-Lima
Journal:  Stat Methods Med Res       Date:  2014-05-20       Impact factor: 3.021

5.  Evaluation of longitudinal surrogate markers.

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

Review 6.  Joint latent class models for longitudinal and time-to-event data: a review.

Authors:  Cécile Proust-Lima; Mbéry Séne; Jeremy M G Taylor; Hélène Jacqmin-Gadda
Journal:  Stat Methods Med Res       Date:  2012-04-19       Impact factor: 3.021

7.  Bayesian Parametric Accelerated Failure Time Spatial Model and its Application to Prostate Cancer.

Authors:  Jiajia Zhang; Andrew B Lawson
Journal:  J Appl Stat       Date:  2011-03       Impact factor: 1.404

8.  Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach.

Authors:  Cécile Proust-Lima; Jeremy M G Taylor
Journal:  Biostatistics       Date:  2009-04-15       Impact factor: 5.899

9.  Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington's disease.

Authors:  Jeffrey D Long; James A Mills
Journal:  BMC Med Res Methodol       Date:  2018-11-16       Impact factor: 4.615

10.  Reflection on modern methods: Dynamic prediction using joint models of longitudinal and time-to-event data.

Authors:  Eleni-Rosalina Andrinopoulou; Michael O Harhay; Sarah J Ratcliffe; Dimitris Rizopoulos
Journal:  Int J Epidemiol       Date:  2021-11-10       Impact factor: 7.196

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