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Statistics in Biosciences

, Volume 11, Issue 3, pp 659–676 | Cite as

Estimating Attributable Life Expectancy Under the Proportional Mean Residual Life Model

  • Yixin Wang
  • Ying Qing ChenEmail author
Article
  • 43 Downloads

Abstract

In population-based health research, the so-called population attributable fraction is an important quantity that calculates the percentage of excess risk of morbidity and mortality associated with modifiable risk factors for a given population. While the concept of “risk” is usually measured by event probabilities, in practice it may be of a more direct interest to know the excess life expectancy associated with the modifiable risk factors instead, particularly when mortality is of the ultimate concern. In this paper, we thus propose to study a novel quantity, termed “attributable life expectancy,” to measure the population attributable fraction of life expectancy. We further develop a model-based approach for the attributable life expectancy under the Oakes–Dasu proportional mean residual life model, and establish its asymptotic properties for inferences. Numerical studies that include Monte-Carlo simulations and an actual analysis of the mortality associated with smoking cessation in an Asia Cohort Consortium are conducted to evaluate the performance of our proposed method.

Keywords

Excess life expectancy Population research Residual life regression Time-to-event 

Notes

Acknowledgements

We would like to thank the Editor-in-Chief Professor Mei-Cheng Wang, associate editor, and two anonymous reviewers for their constructive comments that led to a significant improvement of this paper. This research was partially supported by NIH/NCI R01 CA172415 and NIH/NIMH R01 MH105857.

Supplementary material

12561_2019_9258_MOESM1_ESM.pdf (161 kb)
Supplementary Materials: The online supplementary materials include the proofs for Theorem 1. (pdf 162KB)

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Copyright information

© International Chinese Statistical Association 2019

Authors and Affiliations

  1. 1.Fred Hutchinson Cancer Research CenterSeattleUSA

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