Literature DB >> 373811

The analysis of failure times in the presence of competing risks.

R L Prentice, J D Kalbfleisch, A V Peterson, N Flournoy, V T Farewell, N E Breslow.   

Abstract

Distinct problems in the analysis of failure times with competing causes of failure include the estimation of treatment or exposure effects on specific failure types, the study of interrelations among failure types, and the estimation of failure rates for some causes given the removal of certain other failure types. The usual formation of these problems is in terms of conceptual or latent failure times for each failure type. This approach is criticized on the basis of unwarranted assumptions, lack of physical interpretation and identifiability problems. An alternative approach utilizing cause-specific hazard functions for observable quantities, including time-dependent covariates, is proposed. Cause-specific hazard functions are shown to be the basic estimable quantities in the competing risks framework. A method, involving the estimation of parameters that relate time-dependent risk indicators for some causes to cause-specific hazard functions for other causes, is proposed for the study of interrelations among failure types. Further, it is argued that the problem of estimation of failure rates under the removal of certain causes is not well posed until a mechanism for cause removal is specified. Following such a specification, one will sometimes be in a position to make sensible extrapolations from available data to situations involving cause removal. A clinical program in bone marrow transplantation for leukemia provides a setting for discussion and illustration of each of these ideas. Failure due to censoring in a survivorship study leads to further discussion.

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Year:  1978        PMID: 373811

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  373 in total

1.  Comparing sub-survival functions in a competing risks model.

Authors:  K C Carriere; S C Kochar
Journal:  Lifetime Data Anal       Date:  2000-03       Impact factor: 1.588

2.  Gains in life expectancy after elimination of major causes of death: revised estimates taking into account the effect of competing causes.

Authors:  J P Mackenbach; A E Kunst; H Lautenbach; Y B Oei; F Bijlsma
Journal:  J Epidemiol Community Health       Date:  1999-01       Impact factor: 3.710

3.  Group and within-group variable selection for competing risks data.

Authors:  Kwang Woo Ahn; Anjishnu Banerjee; Natasha Sahr; Soyoung Kim
Journal:  Lifetime Data Anal       Date:  2017-08-04       Impact factor: 1.588

4.  The competing risks Cox model with auxiliary case covariates under weaker missing-at-random cause of failure.

Authors:  Daniel Nevo; Reiko Nishihara; Shuji Ogino; Molin Wang
Journal:  Lifetime Data Anal       Date:  2017-08-04       Impact factor: 1.588

5.  Assessing treatment benefit with competing risks not affected by the randomized treatment.

Authors:  Edward L Korn; James J Dignam; Boris Freidlin
Journal:  Stat Med       Date:  2014-11-03       Impact factor: 2.373

6.  Trends in the incidence of atrial fibrillation in older patients initiating dialysis in the United States.

Authors:  Benjamin A Goldstein; Cristina M Arce; Mark A Hlatky; Mintu Turakhia; Soko Setoguchi; Wolfgang C Winkelmayer
Journal:  Circulation       Date:  2012-10-02       Impact factor: 29.690

7.  Predictors of locoregional recurrence after neoadjuvant chemotherapy: results from combined analysis of National Surgical Adjuvant Breast and Bowel Project B-18 and B-27.

Authors:  Eleftherios P Mamounas; Stewart J Anderson; James J Dignam; Harry D Bear; Thomas B Julian; Charles E Geyer; Alphonse Taghian; D Lawrence Wickerham; Norman Wolmark
Journal:  J Clin Oncol       Date:  2012-10-01       Impact factor: 44.544

8.  Recovery of Pulmonary Function after Allogeneic Hematopoietic Cell Transplantation in Children is Associated with Improved Survival.

Authors:  Ashok Srinivasan; Anusha Sunkara; William Mitchell; Sudeep Sunthankar; Guolian Kang; Dennis C Stokes; Saumini Srinivasan
Journal:  Biol Blood Marrow Transplant       Date:  2017-09-01       Impact factor: 5.742

9.  A semiparametric censoring bias model for estimating the cumulative risk of a false-positive screening test under dependent censoring.

Authors:  Rebecca A Hubbard; Diana L Miglioretti
Journal:  Biometrics       Date:  2013-02-05       Impact factor: 2.571

10.  Joint Modeling of Repeated Measures and Competing Failure Events In a Study of Chronic Kidney Disease.

Authors:  Wei Yang; Dawei Xie; Qiang Pan; Harold I Feldman; Wensheng Guo
Journal:  Stat Biosci       Date:  2016-12-27
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