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Activity Number: 635
Type: Invited
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #318043 View Presentation
Title: Combining Information from Two Data Sources with Misreporting and Incompleteness to Assess Hospice-Use Among Cancer Patients: A Multiple Imputation Approach
Author(s): Yulei He* and Mary-Beth Landrum and Alan M. Zaslavsky
Companies: CDC and Harvard Medical School and Harvard Medical School
Keywords: data augmentation ; health services research ; measurement error ; model diagnostics ; multilevel models

Combining information from multiple data sources can enhance estimates of health-related measures by using one source to supply information that is lacking in another, assuming the former has accurate and complete data. However, there is little research conducted on combining methods when each sourcemight be imperfect, for example, subject to measurement errors and/or missing data. In a multisite study of hospice-use by late-stage cancer patients, this variable was available from patients' abstracted medical records, which may be considerably underreported because of incomplete acquisition of these records. Therefore, data for Medicare-eligible patients were supplemented with their Medicare claims that contained information on hospice-use, which may also be subject to underreporting yet to a lesser degree. In addition, both sources suffered from missing data because of unit nonresponse from medical record abstraction and sample undercoverage for Medicare claims. We treat the true hospice-use status from these patients as a latent variable and propose to multiply impute it using information from both data sources, borrowing the strength from each data source.

Authors who are presenting talks have a * after their name.

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