Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data
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- 作者：Hiraku Kumamaru ; Sebastian Schneeweiss ; Robert J. Glynn…
- 关键词：High dimensional propensity score ; Disease risk score ; Historical data ; Shrinkage ; Comparative study ; Confounding
- 刊名：Emerging Themes in Epidemiology
- 出版年：2016
- 出版时间：December 2016
- 年：2016
- 卷：13
- 期：1
- 全文大小：938 KB
- 刊物主题：Epidemiology;
- 出版者：BioMed Central
- ISSN：1742-7622
文摘
Background Multivariable confounder adjustment in comparative studies of newly marketed drugs can be limited by small numbers of exposed patients and even fewer outcomes. Disease risk scores (DRSs) developed in historical comparator drug users before the new drug entered the market may improve adjustment. However, in a high dimensional data setting, empirical selection of hundreds of potential confounders and modeling of DRS even in the historical cohort can lead to over-fitting and reduced predictive performance in the study cohort. We propose the use of combinations of dimension reduction and shrinkage methods to overcome this problem, and compared the performances of these modeling strategies for implementing high dimensional (hd) DRSs from historical data in two empirical study examples of newly marketed drugs versus comparator drugs after the new drugs’ market entry—dabigatran versus warfarin for the outcome of major hemorrhagic events and cyclooxygenase-2 inhibitor (coxibs) versus nonselective non-steroidal anti-inflammatory drugs (nsNSAIDs) for gastrointestinal bleeds.