Literature DB >> 7820293

Effects of frailty in survival analysis.

O O Aalen1.   

Abstract

Unobserved individual heterogeneity, also called frailty, is a major concern in the application of survival analysis. Hazard rates do not give direct information on the change over time in the individual risk, but are strongly influenced by selection effects operating in the population. The individuals surviving up to a certain time will on average be less frail than the original population. Models are reviewed that account for this phenomenon, and some medical examples are discussed. It is emphasized that the frailty phenomenon may be modelled in many different ways, and a stochastic process approach is discussed as an alternative to the common proportional frailty model.

Mesh:

Year:  1994        PMID: 7820293     DOI: 10.1177/096228029400300303

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  39 in total

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2.  Frailty modelling of colorectal cancer incidence in Norway: indications that individual heterogeneity in risk is related to birth cohort.

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4.  Modeling two-state disease processes with random effects.

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Review 5.  Frailty models for survival data.

Authors:  P Hougaard
Journal:  Lifetime Data Anal       Date:  1995       Impact factor: 1.588

6.  Modelling conditional distributions in bivariate survival.

Authors:  R Henderson
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7.  Prevalent cohort studies and unobserved heterogeneity.

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8.  Markov chains and semi-Markov models in time-to-event analysis.

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Review 9.  How the effects of aging and stresses of life are integrated in mortality rates: insights for genetic studies of human health and longevity.

Authors:  Anatoliy I Yashin; Konstantin G Arbeev; Liubov S Arbeeva; Deqing Wu; Igor Akushevich; Mikhail Kovtun; Arseniy Yashkin; Alexander Kulminski; Irina Culminskaya; Eric Stallard; Miaozhu Li; Svetlana V Ukraintseva
Journal:  Biogerontology       Date:  2015-08-18       Impact factor: 4.277

10.  Predictors of mortality of patients newly diagnosed with clinical type 2 diabetes: a 5-year follow up study.

Authors:  Niels de Fine Olivarius; Volkert Siersma; Anni Bs Nielsen; Lars J Hansen; Lotte Rosenvinge; Carl Erik Mogensen
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