Literature DB >> 24729136

Semicompeting risks in aging research: methods, issues and needs.

Ravi Varadhan1, Qian-Li Xue, Karen Bandeen-Roche.   

Abstract

A semicompeting risks problem involves two-types of events: a nonterminal and a terminal event (death). Typically, the nonterminal event is the focus of the study, but the terminal event can preclude the occurrence of the nonterminal event. Semicompeting risks are ubiquitous in studies of aging. Examples of semicompeting risk dyads include: dementia and death, frailty syndrome and death, disability and death, and nursing home placement and death. Semicompeting risk models can be divided into two broad classes: models based only on observables quantities (class [Formula: see text]) and those based on potential (latent) failure times (class [Formula: see text]). The classical illness-death model belongs to class [Formula: see text]. This model is a special case of the multistate models, which has been an active area of methodology development. During the past decade and a half, there has also been a flurry of methodological activity on semicompeting risks based on latent failure times ([Formula: see text] models). These advances notwithstanding, the semicompeting risks methodology has not penetrated biomedical research, in general, and gerontological research, in particular. Some possible reasons for this lack of uptake are: the methods are relatively new and sophisticated, conceptual problems associated with potential failure time models are difficult to overcome, paucity of expository articles aimed at educating practitioners, and non-availability of readily usable software. The main goals of this review article are: (i) to describe the major types of semicompeting risks problems arising in aging research, (ii) to provide a brief survey of the semicompeting risks methods, (iii) to suggest appropriate methods for addressing the problems in aging research, (iv) to highlight areas where more work is needed, and (v) to suggest ways to facilitate the uptake of the semicompeting risks methodology by the broader biomedical research community.

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Year:  2014        PMID: 24729136      PMCID: PMC4430119          DOI: 10.1007/s10985-014-9295-7

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  41 in total

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Authors:  Constantine E Frangakis; Donald B Rubin; Ming-Wen An; Ellen MacKenzie
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2.  A simple stochastic model of recovery, relapse, death and loss of patients.

Authors:  E FIX; J NEYMAN
Journal:  Hum Biol       Date:  1951-09       Impact factor: 0.553

3.  Prediction of risk of falling, physical disability, and frailty by rate of decline in grip strength: the women's health and aging study.

Authors:  Qian-Li Xue; Jeremy D Walston; Linda P Fried; Brock A Beamer
Journal:  Arch Intern Med       Date:  2011-06-27

4.  Maximum likelihood analysis of semicompeting risks data with semiparametric regression models.

Authors:  Yi-Hau Chen
Journal:  Lifetime Data Anal       Date:  2011-08-18       Impact factor: 1.588

5.  An analytic method for randomized trials with informative censoring: Part 1.

Authors:  J M Robins
Journal:  Lifetime Data Anal       Date:  1995       Impact factor: 1.588

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

Authors:  R L Prentice; J D Kalbfleisch; A V Peterson; N Flournoy; V T Farewell; N E Breslow
Journal:  Biometrics       Date:  1978-12       Impact factor: 2.571

7.  PSA screening among elderly men with limited life expectancies.

Authors:  Louise C Walter; Daniel Bertenthal; Karla Lindquist; Badrinath R Konety
Journal:  JAMA       Date:  2006-11-15       Impact factor: 56.272

8.  Cancer screening in elderly patients: a framework for individualized decision making.

Authors:  L C Walter; K E Covinsky
Journal:  JAMA       Date:  2001-06-06       Impact factor: 56.272

9.  Modeling familial association of ages at onset of disease in the presence of competing risk.

Authors:  Joanna H Shih; Paul S Albert
Journal:  Biometrics       Date:  2010-12       Impact factor: 2.571

10.  The hazards of hazard ratios.

Authors:  Miguel A Hernán
Journal:  Epidemiology       Date:  2010-01       Impact factor: 4.822

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  11 in total

1.  Natural history of diseases: Statistical designs and issues.

Authors:  Nicholas P Jewell
Journal:  Clin Pharmacol Ther       Date:  2016-08-18       Impact factor: 6.875

2.  Modeling of semi-competing risks by means of first passage times of a stochastic process.

Authors:  Beate Sildnes; Bo Henry Lindqvist
Journal:  Lifetime Data Anal       Date:  2017-07-22       Impact factor: 1.588

3.  Frailty modelling approaches for semi-competing risks data.

Authors:  Il Do Ha; Liming Xiang; Mengjiao Peng; Jong-Hyeon Jeong; Youngjo Lee
Journal:  Lifetime Data Anal       Date:  2019-02-07       Impact factor: 1.588

4.  A Simulation Platform for Quantifying Survival Bias: An Application to Research on Determinants of Cognitive Decline.

Authors:  Elizabeth Rose Mayeda; Eric J Tchetgen Tchetgen; Melinda C Power; Jennifer Weuve; Hélène Jacqmin-Gadda; Jessica R Marden; Eric Vittinghoff; Niels Keiding; M Maria Glymour
Journal:  Am J Epidemiol       Date:  2016-08-30       Impact factor: 4.897

5.  SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data.

Authors:  Danilo Alvares; Sebastien Haneuse; Catherine Lee; Kyu Ha Lee
Journal:  R J       Date:  2019-08-20       Impact factor: 3.984

6.  Causal inference for semi-competing risks data.

Authors:  Daniel Nevo; Malka Gorfine
Journal:  Biostatistics       Date:  2022-10-14       Impact factor: 5.279

Review 7.  Parametric estimation of association in bivariate failure-time data subject to competing risks: sensitivity to underlying assumptions.

Authors:  Jeongyong Kim; Karen Bandeen-Roche
Journal:  Lifetime Data Anal       Date:  2018-08-03       Impact factor: 1.588

8.  Covariate adjustment using propensity scores for dependent censoring problems in the accelerated failure time model.

Authors:  Youngjoo Cho; Chen Hu; Debashis Ghosh
Journal:  Stat Med       Date:  2017-10-10       Impact factor: 2.373

9.  Covariate adjustment via propensity scores for recurrent events in the presence of dependent censoring.

Authors:  Youngjoo Cho; Debashis Ghosh
Journal:  Commun Stat Theory Methods       Date:  2019-07-15       Impact factor: 0.893

10.  Biased Exposure-Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?

Authors:  Marc G Weisskopf; David Sparrow; Howard Hu; Melinda C Power
Journal:  Environ Health Perspect       Date:  2015-05-08       Impact factor: 9.031

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