A pairwise likelihood augmented Cox estimator for left‐truncated data

A pairwise likelihood augmented Cox estimator for left‐truncated data IntroductionSurvival data collected from a prevalent cohort that includes patients who already have the disease at the study enrollment are subject to left truncation. This is because those who died with the disease before the enrollment would have no chances to be selected, whereas the selected patients, having survived until the enrollment, are healthier on average. To avoid overestimating the survival, conventional approaches make inferences conditional on truncation times (Kalbfleisch and Lawless, ; Wang et al., ). These approaches disregard the information about the regression coefficients in the marginal likelihood of the truncation times, and hence loss of efficiency is expected when additional knowledge on the underlying truncation distribution is available (Huang and Qin, ).If the underlying truncation time is uniformly distributed, left truncation reduces to length‐biased sampling (Vardi, ), that is, the probability of selecting a subject is proportional to the length of his or her underlying failure time; see a comprehensive review by Shen et al. (). Among the newly developed regression methods for length‐biased data, many show considerable improvement of efficiency in estimation compared with the conditional approach by incorporating information from the observed truncation times (Qin and Shen, ; Qin et al., ; Huang et http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

A pairwise likelihood augmented Cox estimator for left‐truncated data

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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.12746
Publisher site
See Article on Publisher Site

Abstract

IntroductionSurvival data collected from a prevalent cohort that includes patients who already have the disease at the study enrollment are subject to left truncation. This is because those who died with the disease before the enrollment would have no chances to be selected, whereas the selected patients, having survived until the enrollment, are healthier on average. To avoid overestimating the survival, conventional approaches make inferences conditional on truncation times (Kalbfleisch and Lawless, ; Wang et al., ). These approaches disregard the information about the regression coefficients in the marginal likelihood of the truncation times, and hence loss of efficiency is expected when additional knowledge on the underlying truncation distribution is available (Huang and Qin, ).If the underlying truncation time is uniformly distributed, left truncation reduces to length‐biased sampling (Vardi, ), that is, the probability of selecting a subject is proportional to the length of his or her underlying failure time; see a comprehensive review by Shen et al. (). Among the newly developed regression methods for length‐biased data, many show considerable improvement of efficiency in estimation compared with the conditional approach by incorporating information from the observed truncation times (Qin and Shen, ; Qin et al., ; Huang et

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

References

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