Statistical Methods for Analyzing Time-Dependent Events in Breast Cancer Chemoprevention Studies.
Abstract
The overall aim of our research proposal is the statistical nonparametric inference of the redistribution-to-the-center estimator (RTCH) and the generalized maximum likelihood estimator (GMLE) for the survival function of a time-to- event variable that is subject to interval censoring. The RTCH, which is proposed by us, has a closed-form expression and is equal to the GMLE under a homogeneous condition. The GMLH is the standard optimal estimator in survival analysis. However, it cannot be expressed in a closed-form expression, and asymptotic distribution theory for it has been limited. From the asymptotic study of the RTCE, we have gained important insight into proofs of asymptotic properties of the GMTE. In the past four years we have established consistency, asymptotic normality and asymptotic efficiency of the GMLE under a variety of conditions. In addition, we have derived an asymptotic nonparametric two-sample distance test procedure for comparing two populations. Finally, we have begun investigating the asymptotic inference of Cox regression model for interval-censored data by establishing consistency and asymptotic normality of the %GMLE of the regression parameters under some finite assumptions. These preliminary results are being applied to a breast cancer prognostic relapse follow-up study.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 1998
- Accession Number
- ADA360834
Entities
People
- George Y. Wong