Standard error estimation using the EM algorithm for the joint modeling of survival and longitudinal data.

ABSTRACT: Joint modeling of survival and longitudinal data has been studied extensively in the recent literature. The likelihood approach is one of the most popular estimation methods employed within the joint modeling framework. Typically, the parameters are estimated using maximum likelihood, with computation performed by the expectation maximization (EM) algorithm. However, one drawback of this approach is that standard error (SE) estimates are not automatically produced when using the EM algorithm. Many different procedures have been proposed to obtain the asymptotic covariance matrix for the parameters when the number of parameters is typically small. In the joint modeling context, however, there may be an infinite-dimensional parameter, the baseline hazard function, which greatly complicates t

SUBMITTER: Xu C 

PROVIDER: S-EPMC4173103 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature