Local EM estimation of the hazard function for interval-censored data

Rebecca A. Betensky, Jane C. Lindsey, Louise M. Ryan, M. P. Wand

Research output: Contribution to journalArticlepeer-review


We propose a smooth hazard estimator for interval-censored survival data using the method of local likelihood. The model is fit using a local EM algorithm. The estimator is more descriptive than traditional empirical estimates in regions of concentrated information and takes on a parametric flavor in regions of sparse information. We derive two different standard error estimates for the smooth curve, one based on asymptotic theory and the other on the bootstrap. We illustrate the local EM method for times to breast cosmesis deterioration (Finkelstein, 1986, Biometrics 42, 845-854) and for times to HIV-1 infection for individuals with hemophilia (Kroner et al., 1994, Journal of AIDS 7, 279-286). Our hazard estimates for each of these data sets show interesting structures that would not be found using a standard parametric hazard model or empirical survivorship estimates.

Original languageEnglish (US)
Pages (from-to)238-245
Number of pages8
Issue number1
StatePublished - Mar 1999


  • EM algorithm
  • Estimating equations
  • Local likelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


Dive into the research topics of 'Local EM estimation of the hazard function for interval-censored data'. Together they form a unique fingerprint.

Cite this