Individualized Strategies for Breast Cancer Surveillance Based on Aggregated Familial Information
Abstract
This final report is concerned with stochastic modeling of breast cancer detection and estimation problems associated with the two-variate distribution of age and tumor size at diagnosis. Our methodological approach is designed to accommodate generally-structured data. Another research avenue was related to optimal scheduling of breast cancer screening by maximizing the expected reduction of tumor size at detection. We developed a Monte- Carlo EM algorithm for estimation of biologically meaningful parameters incorporated into the joint distribution of age and tumor size at detection. The proposed estimation techniques were tested by computer simulations and applied to epidemiological data on individuals identified through the Utah Population Database (UPDB) and Utah Cancer Registry. We studied various indicators of family history and used one of them to stratify the data on breast cancer obtained from the UPDB. An optimal schedule has been constructed for low- and high-risk groups of individuals identified through the UPDB. While the efficacy of the optimal schedule tends to be higher in high-risk families, its structure appears to be robust to variations in breast cancer risk.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2002
- Accession Number
- ADA407384
Entities
People
- Alexander B. Yakovlev
- Alexander Tsodikov
- G. Gregori
- K. Boucher
- R. Kerber
Organizations
- University of Utah