Simple and fast overidentified rank estimation for right‐censored length‐biased data and backward recurrence time

Simple and fast overidentified rank estimation for right‐censored length‐biased data and... IntroductionThe accelerated failure time (AFT) model is an important alternative to Cox's proportional hazards model and is particularly appealing to medical investigators due to its straightforward interpretation. In an ideal situation, prospective follow‐up studies are conducted by sampling incident cases over a possibly long period, and the subsequent survival time of interest is usually subject to right censoring. Methods for AFT model for traditional right‐censored survival data has been extensively studied by many authors, see, Buckley and James (), Tsiatis (), Ying () among others. In practice, due to constraints on cost and time, studies on incident cohorts are often unavailable, and data on a prevalent cohort of diseased individuals, who have experienced the disease incidence before recruitment but not the failure event, are collected and analyzed. For example, in the Canadian Study of Health and Aging (CSHA), survival data were collected from a prevalent cohort of dementia patients who were alive at the time of recruitment. In many applications, including the CSHA, it is reasonable to assume that the incidence of disease onset is stable over time, and the survival time in the prevalent cohort is length‐biased (Wang, ; Asgharian et al., ).Semiparametric estimation of the AFT model http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Simple and fast overidentified rank estimation for right‐censored length‐biased data and backward recurrence time

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12727
Publisher site
See Article on Publisher Site

Abstract

IntroductionThe accelerated failure time (AFT) model is an important alternative to Cox's proportional hazards model and is particularly appealing to medical investigators due to its straightforward interpretation. In an ideal situation, prospective follow‐up studies are conducted by sampling incident cases over a possibly long period, and the subsequent survival time of interest is usually subject to right censoring. Methods for AFT model for traditional right‐censored survival data has been extensively studied by many authors, see, Buckley and James (), Tsiatis (), Ying () among others. In practice, due to constraints on cost and time, studies on incident cohorts are often unavailable, and data on a prevalent cohort of diseased individuals, who have experienced the disease incidence before recruitment but not the failure event, are collected and analyzed. For example, in the Canadian Study of Health and Aging (CSHA), survival data were collected from a prevalent cohort of dementia patients who were alive at the time of recruitment. In many applications, including the CSHA, it is reasonable to assume that the incidence of disease onset is stable over time, and the survival time in the prevalent cohort is length‐biased (Wang, ; Asgharian et al., ).Semiparametric estimation of the AFT model

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ;

References

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