Flexible Modeling of Survival Data with Covariates Subject to Detection Limits via Multiple Imputation.

ABSTRACT: Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time model with a flexible seminonparametric error distribution. The consistency and asymptotic normality of the multiple imputation estimator are established and a consistent variance estimator is provided. An iterative version of the proposed multiple imputation algorithm that approximates the EM algorithm for maximum likelihood is also suggested. Simulation studies demonstrate that the proposed multip

SUBMITTER: Bernhardt PW 

PROVIDER: S-EPMC3816712 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature