NDLI: Backward multiple imputation estimation of the conditional lifetime expectancy function with application to censored human longevity data.

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Backward multiple imputation estimation of the conditional lifetime expectancy function with application to censored human longevity data.

Content Provider	Semantic Scholar
Author	Kong, Jing Klein, Barbara E. K. Klein, Ronald Wahba, Grace
Copyright Year	2015
Abstract	The conditional lifetime expectancy function (LEF) is the expected lifetime of a subject given survival past a certain time point and the values of a set of explanatory variables. This function is attractive to researchers because it summarizes the entire residual life distribution and has an easy interpretation compared with the popularly used hazard function. In this paper, we propose a general framework of backward multiple imputation for estimating the conditional LEF and the variance of the estimator in the right-censoring setting. Simulation studies are conducted to investigate the empirical properties of the proposed estimator and the corresponding variance estimator. We demonstrate the method on the Beaver Dam Eye Study data, where the expected human lifetime is modeled with smoothing-spline ANOVA given the covariates information including sex, lifestyle factors, and disease variables.
File Format	PDF HTM / HTML
DOI	10.1073/pnas.1512237112
PubMed reference number	26371300
Journal	Medline
Volume Number	112
Issue Number	39
Alternate Webpage(s)	http://www.stat.wisc.edu/~wahba/ftp1/PNAS-2015-Kong-1512237112.pdf
Alternate Webpage(s)	http://www.pnas.org/content/112/39/12069.full.pdf
Alternate Webpage(s)	https://doi.org/10.1073/pnas.1512237112
Journal	Proceedings of the National Academy of Sciences of the United States of America
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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