Literature DB >> 28160312

Cox proportional hazards models with left truncation and time-varying coefficient: Application of age at event as outcome in cohort studies.

Minjin Kim1, Myunghee Cho Paik1, Jiyeong Jang2, Ying K Cheung3, Joshua Willey4, Mitchell S V Elkind4, Ralph L Sacco5.   

Abstract

When analyzing time-to-event cohort data, two different ways of choosing a time scale have been discussed in the literature: time-on-study or age at onset of disease. One advantage of choosing the latter is interpretability of the hazard ratio as a function of age. To handle the analysis of age at onset in a principled manner, we present an analysis of the Cox Proportional Hazards model with time-varying coefficient for left-truncated and right-censored data. In the analysis of Northern Manhattan Study (NOMAS) with age at onset of stroke as outcome, we demonstrate that well-established risk factors may be important only around a certain age span and less established risk factors can have a strong effect in a certain age span.
© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Estimating equation; Local linear fitting; Profile likelihood; Time-to-event cohort data; Time-varying coefficient

Mesh:

Year:  2017        PMID: 28160312      PMCID: PMC7039372          DOI: 10.1002/bimj.201600003

Source DB:  PubMed          Journal:  Biom J        ISSN: 0323-3847            Impact factor:   2.207


  9 in total

1.  Model-checking techniques based on cumulative residuals.

Authors:  D Y Lin; L J Wei; Z Ying
Journal:  Biometrics       Date:  2002-03       Impact factor: 2.571

2.  Choice of time scale and its effect on significance of predictors in longitudinal studies.

Authors:  Michael J Pencina; Martin G Larson; Ralph B D'Agostino
Journal:  Stat Med       Date:  2007-03-15       Impact factor: 2.373

3.  Time-to-event analysis of longitudinal follow-up of a survey: choice of the time-scale.

Authors:  E L Korn; B I Graubard; D Midthune
Journal:  Am J Epidemiol       Date:  1997-01-01       Impact factor: 4.897

4.  Social determinants of physical inactivity in the Northern Manhattan Study (NOMAS).

Authors:  Joshua Z Willey; Myunghee C Paik; Ralph Sacco; Mitchell S V Elkind; Bernadette Boden-Albala
Journal:  J Community Health       Date:  2010-12

5.  Spline-based tests in survival analysis.

Authors:  R J Gray
Journal:  Biometrics       Date:  1994-09       Impact factor: 2.571

6.  Causal estimation using semiparametric transformation models under prevalent sampling.

Authors:  Yu-Jen Cheng; Mei-Cheng Wang
Journal:  Biometrics       Date:  2015-02-25       Impact factor: 2.571

7.  SEMIPARAMETRIC REGRESSION WITH TIME-DEPENDENT COEFFICIENTS FOR FAILURE TIME DATA ANALYSIS.

Authors:  Zhangsheng Yu; Xihong Lin
Journal:  Stat Sin       Date:  2010-04-01       Impact factor: 1.261

8.  Comments on 'Choice of time scale and its effect on significance of predictors in longitudinal studies' by Michael J. Pencina, Martin G. Larson and Ralph B. D'Agostino, Statistics in Medicine 2007; 26:1343-1359.

Authors:  Mitchell H Gail; Barry Graubard; David F Williamson; Katherine M Flegal
Journal:  Stat Med       Date:  2009-04-15       Impact factor: 2.373

9.  A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation.

Authors:  Willi Sauerbrei; Patrick Royston; Maxime Look
Journal:  Biom J       Date:  2007-06       Impact factor: 2.207

  9 in total
  1 in total

1.  Preexisting Bipolar Disorder Influences the Subsequent Phenotype of Parkinson's Disease.

Authors:  Marco Onofrj; Angelo Di Iorio; Claudia Carrarini; Mirella Russo; Raffaella Franciotti; Alberto J Espay; Laura S Boylan; John-Paul Taylor; Massimo Di Giannantonio; Giovanni Martinotti; Enza M Valente; Astrid Thomas; Laura Bonanni; Stefano Delli Pizzi; Fedele Dono; StefanoL Sensi
Journal:  Mov Disord       Date:  2021-08-24       Impact factor: 9.698

  1 in total

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