Imputing underreported treatments using multiple sources of treatment information in a cancer services study
Yulei He, Department of Health Care Policy, Harvard Medical School
*Alan M. Zaslavsky, Harvard University
Keywords: imputation, multivariate probit, cancer, bayesian model, health services, measurement error
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.