Literature DB >> 16755533

Misspecified regression model for the subdistribution hazard of a competing risk.

A Latouche1, V Boisson, S Chevret, R Porcher.   

Abstract

We consider a competing risks setting, when evaluating the prognostic influence of an exposure on a specific cause of failure. Two main regression models are used in such analyses, the Cox cause-specific proportional hazards model and the subdistribution proportional hazards model. They are exemplified in a real data example focusing on relapse-free interval in acute leukaemia patients. We examine the properties of the estimator based on the latter model when the true model is the former. An explicit relationship between subdistribution hazards ratio and cause-specific hazards ratio is derived, assuming a flexible parametric distribution for latent failure times. Copyright (c) 2006 John Wiley & Sons, Ltd.

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Year:  2007        PMID: 16755533     DOI: 10.1002/sim.2600

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  28 in total

1.  The use and interpretation of competing risks regression models.

Authors:  James J Dignam; Qiang Zhang; Masha Kocherginsky
Journal:  Clin Cancer Res       Date:  2012-01-26       Impact factor: 12.531

Review 2.  Applying competing risks regression models: an overview.

Authors:  Bernhard Haller; Georg Schmidt; Kurt Ulm
Journal:  Lifetime Data Anal       Date:  2012-09-26       Impact factor: 1.588

3.  A semiparametric random effects model for multivariate competing risks data.

Authors:  Thomas H Scheike; Yanqing Sun; Mei-Jie Zhang; Tina Kold Jensen
Journal:  Biometrika       Date:  2010-03       Impact factor: 2.445

4.  Nonparametric Assessment of Differences Between Competing Risk Hazard Ratios: Application to Racial Differences in Pediatric Chronic Kidney Disease Progression.

Authors:  Derek K Ng; Daniel A Antiporta; Matthew B Matheson; Alvaro Muñoz
Journal:  Clin Epidemiol       Date:  2020-01-20       Impact factor: 4.790

5.  Joint Inference for Competing Risks Survival Data.

Authors:  Gang Li; Qing Yang
Journal:  J Am Stat Assoc       Date:  2016-10-18       Impact factor: 5.033

6.  A proportional hazards regression model for the subdistribution with right-censored and left-truncated competing risks data.

Authors:  Xu Zhang; Mei-Jie Zhang; Jason Fine
Journal:  Stat Med       Date:  2011-05-09       Impact factor: 2.373

7.  Patient death as a censoring event or competing risk event in models of nursing home placement.

Authors:  Jeff M Szychowski; David L Roth; Olivio J Clay; Mary S Mittelman
Journal:  Stat Med       Date:  2010-02-10       Impact factor: 2.373

8.  Competing risk regression models for epidemiologic data.

Authors:  Bryan Lau; Stephen R Cole; Stephen J Gange
Journal:  Am J Epidemiol       Date:  2009-06-03       Impact factor: 4.897

9.  Direct likelihood inference on the cause-specific cumulative incidence function: A flexible parametric regression modelling approach.

Authors:  Sarwar Islam Mozumder; Mark Rutherford; Paul Lambert
Journal:  Stat Med       Date:  2017-10-02       Impact factor: 2.373

10.  Impact of comorbidities at diagnosis on prostate cancer treatment and survival.

Authors:  Katarina Luise Matthes; Manuela Limam; Giulia Pestoni; Leonhard Held; Dimitri Korol; Sabine Rohrmann
Journal:  J Cancer Res Clin Oncol       Date:  2018-02-07       Impact factor: 4.553

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