Cox regression with linked data
Résumé
Record linkage is increasingly used, especially in medical studies, to combine data from different databases that refer to the same entities. The linked data can bring analysts novel and valuable knowledge that is impossible to obtain from a single database. However, linkage errors are usually unavoidable regardless of record linkage methods and ignoring these errors may lead to bias estimates. While different methods have been developed to deal with the linkage errors in the generalized linear model, there is not much interest on Cox regression model although this is one of the most important statistical models in clinical and epidemiological research. In this work, we propose an adjusted estimating equation for secondary Cox regression analysis, where linked data have been prepared by someone else and no information on matching variables is available to the analyst. Through a Monte Carlo simulation study, the proposed method has significantly corrected the parameter estimate bias of the Cox model caused by false links. An asymptotically unbiased variance estimator for the adjusted estimators of Cox regression coefficients is also proposed. Finally, the proposed method will be applied to a linked database from the Brest stroke registry in France.
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