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

Journal Article (Journal Article)

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 censoring. The formulation allows popular models, such as the proportional hazards, proportional odds, and accelerated failure time models, to be placed in a common framework; provides a principled basis for choosing among them; and renders useful extensions of the models straightforward. The utility and performance of the methods are demonstrated via simulations and by application to data from time-to-event studies.

Full Text

Duke Authors

Cited Authors

  • Zhang, M; Davidian, M

Published Date

  • June 2008

Published In

Volume / Issue

  • 64 / 2

Start / End Page

  • 567 - 576

PubMed ID

  • 17970813

Pubmed Central ID

  • PMC2575078

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

Digital Object Identifier (DOI)

  • 10.1111/j.1541-0420.2007.00928.x


  • eng

Conference Location

  • United States