数据融合的效率得与失.pdf

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1、.Multiply robust estimation of causal effects usinglinked dataShanshan Luo1,Yechi Zhang2,and Wei Li21School of Mathematics and StatisticsBeijing Technology and Business University2Center for Applied Statistics and School of StatisticsRenmin University of ChinaShanshan Luo(BTBU)Robust estimation usin

2、g linked data2023.10.211/57.Table of Contents1Introduction2Set up3Estimation4Design Issue5Numerical studies6ConclusionsShanshan Luo(BTBU)Robust estimation using linked data2023.10.212/57.Table of Contents1Introduction2Set up3Estimation4Design Issue5Numerical studies6ConclusionsShanshan Luo(BTBU)Robu

3、st estimation using linked data2023.10.213/57.Unmeasured Confounding and Data Linkage IUnmeasured confounding remains a persistent challenge withinobservational studies,leading to biased estimations of causalparameters.In the current era of big data,the increasing availability of diversedata sources

4、 offers potential remedies.Among these,leveraging data linkage emerges as a promisingapproach to mitigate the impact of unmeasured confounding in aprimary study of interest.Shanshan Luo(BTBU)Robust estimation using linked data2023.10.214/57.Unmeasured Confounding and Data Linkage IIFor instance,in h

5、ealthcare research,the linkage of a claims databasefrom a health plan with an electronic health record database from adelivery system can yield richer patient data.The resulting linked cohort,comprising patients present in both datasources,presents an opportunity to enhance estimation byincorporatin

6、g pivotal confounding factors.Shanshan Luo(BTBU)Robust estimation using linked data2023.10.215/57.ChallengesHowever,the data linkage approach may introduce selection bias,which arises from the fact that studies conducted within linkeddatabases are often restricted to a subset of the primary studypop