JSM Preliminary Online Program
This is the preliminary program for the 2007 Joint Statistical Meetings in Salt Lake City, Utah.

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Activity Number: 227
Type: Invited
Date/Time: Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
Sponsor: Section on Health Policy Statistics
Abstract - #307843
Title: Imputing Under-Reported Treatments Using Multiple Sources of Treatment Information in a Cancer Services Study
Author(s): Alan M. Zaslavsky*+ and Yulei He
Companies: Harvard University and Harvard Medical School
Address: Health Care Policy , Boston, MA, 02115,
Keywords: imputation ; multivariate probit ; cancer ; bayesian model ; health services ; measurement error
Abstract:

Cancer registry records, patient surveys, and administrative systems record adjuvant therapies (chemo and radiation) for cancer patients, but subject to underreporting that could bias analyses. We propose to impute true treatment status, using sample validation data from medical records, and analyze the imputed data. We extend an earlier study with a single outcome (provision of chemotherapy) and base data system (the registry), to multiple measures (provision of chemotherapy and radiation therapy) and multiple data systems (the registry, a patient survey, and Medicare claims). Bayesian hierarchical models for provision and reporting of multiple cancer therapies take into account their associations and multilevel structure, using related multivariate probit models for reporting of each therapy. The methodology is applied to data for patients with colorectal cancer in California.


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Revised September, 2007