Literature DB >> 20802852

ASSESSING THE ASSOCIATION BETWEEN TRENDS IN A BIOMARKER AND RISK OF EVENT WITH AN APPLICATION IN PEDIATRIC HIV/AIDS.

Elizabeth R Brown1.   

Abstract

We present a new joint longitudinal and survival model aimed at estimating the association between the risk of an event and the change in and history of a biomarker that is repeatedly measured over time. We use cubic B-splines models for the longitudinal component that lend themselves to straight-forward formulations of the slope and integral of the trajectory of the biomarker. The model is applied to data collected in a long term follow-up study of HIV infected infants in Uganda. Estimation is carried out using MCMC methods. We also explore using the deviance information criteria, the conditional predictive ordinate and ROC curves for model selection and evaluation.

Entities:  

Year:  2009        PMID： 20802852      PMCID： PMC2928653          DOI： 10.1214/09-aoas251

Source DB:  PubMed          Journal:  Ann Appl Stat        ISSN： 1932-6157            Impact factor:   2.083


  10 in total

1.  Time-dependent ROC curves for censored survival data and a diagnostic marker.

Authors:  P J Heagerty; T Lumley; M S Pepe
Journal:  Biometrics       Date:  2000-06       Impact factor: 2.571

2.  A flexible B-spline model for multiple longitudinal biomarkers and survival.

Authors:  Elizabeth R Brown; Joseph G Ibrahim; Victor DeGruttola
Journal:  Biometrics       Date:  2005-03       Impact factor: 2.571

3.  A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome.

Authors:  Cécile Proust-Lima; Luc Letenneur; Hélène Jacqmin-Gadda
Journal:  Stat Med       Date:  2007-05-10       Impact factor: 2.373

4.  Prognostic value of HIV-1 RNA, CD4 cell count, and CD4 Cell count slope for progression to AIDS and death in untreated HIV-1 infection.

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Journal:  JAMA       Date:  2007-06-06       Impact factor: 56.272

5.  Parametric latent class joint model for a longitudinal biomarker and recurrent events.

Authors:  Jun Han; Elizabeth H Slate; Edsel A Peña
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6.  Semiparametric modeling of longitudinal measurements and time-to-event data--a two-stage regression calibration approach.

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Journal:  Biometrics       Date:  2008-02-07       Impact factor: 2.571

7.  Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach.

Authors:  C L Faucett; D C Thomas
Journal:  Stat Med       Date:  1996-08-15       Impact factor: 2.373

8.  A joint model for survival and longitudinal data measured with error.

Authors:  M S Wulfsohn; A A Tsiatis
Journal:  Biometrics       Date:  1997-03       Impact factor: 2.571

9.  Intrapartum and neonatal single-dose nevirapine compared with zidovudine for prevention of mother-to-child transmission of HIV-1 in Kampala, Uganda: HIVNET 012 randomised trial.

Authors:  L A Guay; P Musoke; T Fleming; D Bagenda; M Allen; C Nakabiito; J Sherman; P Bakaki; C Ducar; M Deseyve; L Emel; M Mirochnick; M G Fowler; L Mofenson; P Miotti; K Dransfield; D Bray; F Mmiro; J B Jackson
Journal:  Lancet       Date:  1999-09-04       Impact factor: 79.321

10.  Intrapartum and neonatal single-dose nevirapine compared with zidovudine for prevention of mother-to-child transmission of HIV-1 in Kampala, Uganda: 18-month follow-up of the HIVNET 012 randomised trial.

Authors:  J Brooks Jackson; Philippa Musoke; Thomas Fleming; Laura A Guay; Danstan Bagenda; Melissa Allen; Clemensia Nakabiito; Joseph Sherman; Paul Bakaki; Maxensia Owor; Constance Ducar; Martina Deseyve; Anthony Mwatha; Lynda Emel; Corey Duefield; Mark Mirochnick; Mary Glenn Fowler; Lynne Mofenson; Paolo Miotti; Maria Gigliotti; Dorothy Bray; Francis Mmiro
Journal:  Lancet       Date:  2003-09-13       Impact factor: 79.321
  10 in total
  6 in total

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2.  Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model.

Authors:  Jue Wang; Sheng Luo
Journal:  Stat Methods Med Res       Date:  2018-10-11       Impact factor: 3.021

3.  Assessing Importance of Biomarkers: a Bayesian Joint Modeling Approach of Longitudinal and Survival Data with Semicompeting Risks.

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Review 4.  Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival.

Authors:  Konstantin G Arbeev; Igor Akushevich; Alexander M Kulminski; Svetlana V Ukraintseva; Anatoliy I Yashin
Journal:  Front Public Health       Date:  2014-11-06

5.  Shared decision making of burdensome surveillance tests using personalized schedules and their burden and benefit.

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Journal:  Stat Med       Date:  2022-02-10       Impact factor: 2.497

6.  Modeling the underlying biological processes in Alzheimer's disease using a multivariate competing risk joint model.

Authors:  Floor M van Oudenhoven; Sophie H N Swinkels; Tobias Hartmann; Dimitris Rizopoulos
Journal:  Stat Med       Date:  2022-05-18       Impact factor: 2.497
  6 in total

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