Statistical Analysis of Multivariate Interval-Censored Data in Breast Cancer Follow-Up Studies
Abstract
The overall objective of our research proposal is nonparametric inference of the joint survival function S(x1, ..., xd) =Pr(X1 > x1, ..., Xd > xd) of d (>= 2) correlated time-to-event variables X1, ..., Xd, each of which is subject to interval censoring. The standard estimator of S is the generalized maximum likelihood estimator (GMLE) S. However, S cannot be expressed in a closed-form expression and its statistical properties have not been studied in the multivariate case. The technical objectives of this pioneer methodological research proposal are to develop asymptotic generalized maximal likelihood (GML) inference of S and to derive efficient computational algorithms for the GML procedure. In our fourth and final year of research, we have implemented a computer software for asymptotic inference of GMLE p of the correlation coefficient p between a pair of the X' variables. When the censoring distribution is continuous, we have numerically established p is not asymptotically normal and we have implemented a bootstrap method for obtaining interval estimator of p. Thus in our four years of research, we have successfully completed the tasks we proposed. The results will be useful to breast cancer researchers pursuing chemoprevention intervention trials involving multiple surrogate endpoint biomarkers, and genetic epidemiologists conducting studies on familial aggregation of breast cancer and related cancers.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2003
- Accession Number
- ADA418647
Entities
People
- George Y. Wong