Literature DB >> 1054494

A nonidentifiability aspect of the problem of competing risks.

A Tsiatis.   

Abstract

For an experimental animal exposed to k greater than 1 possible risks of death R1, R2, ..., Rk, the term i-th potential survival time designates a random variable Yi supposed to represent the age at death of the animal in hypothetical conditions in which Ri is the only possible risk. The probability that Yi will exceed a preassigned t is called the i-th net survival probability. The results of a survival experiment are represented by k "crude" survival functions, empirical counterparts of the probabilities Qi(t) that an animal will survive at least up to the age t and eventually die from Ri. The analysis of a survival experiment aims at estimating the k net survival probabilities using the empirical data on those termed crude. Therorems 1 and 2 establish the relationship between the net and the crude probabilities of survival. In particular, Theorem 2 shows that, without the not directly verifiable assumption that in their joint distribution the variables Y1, Y2, ..., Yk are mutually independent, a given set of crude survival probabilities Qi(t) does not identify the corresponding net probabilities. An example shows that the results of a customary method of analysis, based on the assumption that Y1, Y2, ..., Yk are independent, may have no resemblance to reality.

Mesh:

Year:  1975        PMID: 1054494      PMCID: PMC432231          DOI: 10.1073/pnas.72.1.20

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  91 in total

1.  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

2.  Profile Likelihood and Incomplete Data.

Authors:  Zhiwei Zhang
Journal:  Int Stat Rev       Date:  2010-04-01       Impact factor: 2.217

3.  Tests for comparing mark-specific hazards and cumulative incidence functions.

Authors:  Peter B Gilbert; Ian W McKeague; Yanqing Sun
Journal:  Lifetime Data Anal       Date:  2004-03       Impact factor: 1.588

Review 4.  Competing risks in epidemiology: possibilities and pitfalls.

Authors:  Per Kragh Andersen; Ronald B Geskus; Theo de Witte; Hein Putter
Journal:  Int J Epidemiol       Date:  2012-01-09       Impact factor: 7.196

5.  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

6.  An estimator of the survival function based on the semi-Markov model under dependent censorship.

Authors:  Seung-Yeoun Lee; Wei-Yann Tsai
Journal:  Lifetime Data Anal       Date:  2005-06       Impact factor: 1.588

7.  Bounds for a joint distribution function with fixed sub-distribution functions: Application to competing risks.

Authors:  A V Peterson
Journal:  Proc Natl Acad Sci U S A       Date:  1976-01       Impact factor: 11.205

8.  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

9.  Trends in mortality and graft failure for renal transplant patients.

Authors:  Douglas E Schaubel; John R Jeffery; Yang Mao; Robert Semenciw; Karen Yeates; Stanley S A Fenton
Journal:  CMAJ       Date:  2002-07-23       Impact factor: 8.262

10.  Effects of dependency among causes of death for cause elimination life table strategies.

Authors:  K G Manton; S S Poss
Journal:  Demography       Date:  1979-05
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.