Literature DB >> 16900575

Analysing and interpreting competing risk data.

Melania Pintilie1.   

Abstract

When competing risks are present, two types of analysis can be performed: modelling the cause specific hazard and modelling the hazard of the subdistribution. This paper contrasts these two methods and presents the benefits of each. The interpretation is specific to the analysis performed. When modelling the cause specific hazard, one performs the analysis under the assumption that the competing risks do not exist. This could be beneficial when, for example, the main interest is whether the treatment works in general. In modelling the hazard of the subdistribution, one incorporates the competing risks in the analysis. This analysis compares the observed incidence of the event of interest between groups. The latter analysis is specific to the structure of the observed data and it can be generalized only to another population with similar competing risks. Copyright (c) 2006 John Wiley & Sons, Ltd.

Mesh:

Year:  2007        PMID: 16900575     DOI: 10.1002/sim.2655

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


  61 in total

1.  Cancer-specific mortality in chronic kidney disease: longitudinal follow-up of a large cohort.

Authors:  Pei-Hsuan Weng; Kuan-Yu Hung; Hsien-Liang Huang; Jen-Hau Chen; Pei-Kun Sung; Kuo-Chin Huang
Journal:  Clin J Am Soc Nephrol       Date:  2011-04-21       Impact factor: 8.237

2.  Association of cancer and Alzheimer's disease risk in a national cohort of veterans.

Authors:  Laura Frain; David Swanson; Kelly Cho; David Gagnon; Kun Ping Lu; Rebecca A Betensky; Jane Driver
Journal:  Alzheimers Dement       Date:  2017-07-12       Impact factor: 21.566

3.  Association of carotid atherosclerosis and stiffness with abdominal aortic aneurysm: The atherosclerosis risk in communities (ARIC) study.

Authors:  Lu Yao; Aaron R Folsom; Alvaro Alonso; Pamela L Lutsey; James S Pankow; Weihua Guan; Susan Cheng; Frank A Lederle; Weihong Tang
Journal:  Atherosclerosis       Date:  2018-02-04       Impact factor: 5.162

4.  A causal framework for classical statistical estimands in failure-time settings with competing events.

Authors:  Jessica G Young; Mats J Stensrud; Eric J Tchetgen Tchetgen; Miguel A Hernán
Journal:  Stat Med       Date:  2020-01-27       Impact factor: 2.373

5.  The Competing Risk of Death in Longitudinal Geriatric Outcomes.

Authors:  Terrence E Murphy; Thomas M Gill; Linda S Leo-Summers; Evelyne A Gahbauer; Margaret A Pisani; Lauren E Ferrante
Journal:  J Am Geriatr Soc       Date:  2018-12-08       Impact factor: 5.562

6.  Coronary death and myocardial infarction among Hispanics in the Northern Manhattan Study: exploring the Hispanic paradox.

Authors:  Joshua Z Willey; Carlos J Rodriguez; Yeseon Park Moon; Myunghee C Paik; Marco R Di Tullio; Shunichi Homma; Ralph L Sacco; Mitchell S V Elkind
Journal:  Ann Epidemiol       Date:  2012-03-15       Impact factor: 3.797

7.  Hodgkin lymphoma post-transplant lymphoproliferative disorder: A comparative analysis of clinical characteristics, prognosis, and survival.

Authors:  Aaron S Rosenberg; Andreas K Klein; Robin Ruthazer; Andrew M Evens
Journal:  Am J Hematol       Date:  2016-04-26       Impact factor: 10.047

8.  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

9.  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

10.  Lifetime Risk of Venous Thromboembolism in Two Cohort Studies.

Authors:  Elizabeth J Bell; Pamela L Lutsey; Saonli Basu; Mary Cushman; Susan R Heckbert; Donald M Lloyd-Jones; Aaron R Folsom
Journal:  Am J Med       Date:  2015-11-18       Impact factor: 4.965

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.