Dataset Information

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

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.

SUBMITTER: Kong J

PROVIDER: S-EPMC4593088 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Kong Jing J Klein Barbara E K BE Klein Ronald R Wahba Grace G

Proceedings of the National Academy of Sciences of the United States of America 20150914 39

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 ...[more]

PMID: 26371300

Similar Datasets

Project description:This work presents methods for estimating genotype-specific distributions from genetic epidemiology studies where the event times are subject to right censoring, the genotypes are not directly observed, and the data arise from a mixture of scientifically meaningful subpopulations. Examples of such studies include kin-cohort studies and quantitative trait locus (QTL) studies. Current methods for analyzing censored mixture data include two types of nonparametric maximum likelihood estimators (NPMLEs) which do not make parametric assumptions on the genotype-specific density functions. Although both NPMLEs are commonly used, we show that one is inefficient and the other inconsistent. To overcome these deficiencies, we propose three classes of consistent nonparametric estimators which do not assume parametric density models and are easy to implement. They are based on the inverse probability weighting (IPW), augmented IPW (AIPW), and nonparametric imputation (IMP). The AIPW achieves the efficiency bound without additional modeling assumptions. Extensive simulation experiments demonstrate satisfactory performance of these estimators even when the data are heavily censored. We apply these estimators to the Cooperative Huntington's Observational Research Trial (COHORT), and provide age-specific estimates of the effect of mutation in the Huntington gene on mortality using a sample of family members. The close approximation of the estimated non-carrier survival rates to that of the U.S. population indicates small ascertainment bias in the COHORT family sample. Our analyses underscore an elevated risk of death in Huntington gene mutation carriers compared to non-carriers for a wide age range, and suggest that the mutation equally affects survival rates in both genders. The estimated survival rates are useful in genetic counseling for providing guidelines on interpreting the risk of death associated with a positive genetic testing, and in facilitating future subjects at risk to make informed decisions on whether to undergo genetic mutation testings.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets