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

Kleins Lab // Publications // Sep 29 2015

PubMed ID: 26371300

Author(s): Kong J, Klein BE, Klein R, Wahba G. Backward multiple imputation estimation of the conditional lifetime expectancy function with application to censored human longevity data. Proc Natl Acad Sci U S A. 2015 Sep 29;112(39):12069-74. doi: 10.1073/pnas.1512237112. Epub 2015 Sep 14. PMID 26371300

Journal: Proceedings Of The National Academy Of Sciences Of The United States Of America, Volume 112, Issue 39, Sep 2015

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.