Combining primary cohort data with external aggregate information without assuming comparability.

ABSTRACT: In comparative effectiveness research (CER) for rare types of cancer, it is appealing to combine primary cohort data containing detailed tumor profiles together with aggregate information derived from cancer registry databases. Such integration of data may improve statistical efficiency in CER. A major challenge in combining information from different resources, however, is that the aggregate information from the cancer registry databases could be incomparable with the primary cohort data, which are often collected from a single cancer center or a clinical trial. We develop an adaptive estimation procedure, which uses the combined information to determine the degree of information borrowing from the aggregate data of the external resource. We establish the asymptotic properties of the esti

SUBMITTER: Chen Z 

PROVIDER: S-EPMC8166575 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature