"Smooth" semiparametric regression analysis for arbitrarily censored time-to-event data.

ABSTRACT: A general framework for regression analysis of time-to-event data subject to arbitrary patterns of censoring is proposed. The approach is relevant when the analyst is willing to assume that distributions governing model components that are ordinarily left unspecified in popular semiparametric regression models, such as the baseline hazard function in the proportional hazards model, have densities satisfying mild "smoothness" conditions. Densities are approximated by a truncated series expansion that, for fixed degree of truncation, results in a "parametric" representation, which makes likelihood-based inference coupled with adaptive choice of the degree of truncation, and hence flexibility of the model, computationally and conceptually straightforward with data subject to any pattern of ce

SUBMITTER: Zhang M 

PROVIDER: S-EPMC2575078 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature