Combining primary cohort data with external aggregate information without assuming comparability

Ziqi Chen, Jing Ning, Yu Shen and Jing Qin

Biometrics, 2021, vol. 77, issue 3, 1024-1036

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 estimators and evaluate the finite sample performance via simulation studies. The proposed method yields a substantial gain in statistical efficiency over the conventional method using the primary cohort only, and avoids undesirable biases when the given external information is incomparable to the primary cohort. We apply the proposed method to evaluate the long‐term effect of trimodality treatment to inflammatory breast cancer (IBC) by tumor subtypes, while combining the IBC patient cohort at The University of Texas MD Anderson Cancer Center and the external aggregate information from the National Cancer Data Base.

Date: 2021
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