Nonparametric estimation for censored mixture data with application to the Cooperative Huntington's Observational Research Trial.

ABSTRACT: 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 as

SUBMITTER: Wang Y 

PROVIDER: S-EPMC3905630 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature