Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data

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Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data

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Title: Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data
Author: Kumamaru, Hiraku; Schneeweiss, Sebastian; Glynn, Robert J.; Setoguchi, Soko; Gagne, Joshua J.

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Citation: Kumamaru, Hiraku, Sebastian Schneeweiss, Robert J. Glynn, Soko Setoguchi, and Joshua J. Gagne. 2016. “Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data.” Emerging Themes in Epidemiology 13 (1): 5. doi:10.1186/s12982-016-0047-x. http://dx.doi.org/10.1186/s12982-016-0047-x.
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Abstract: 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. Results: Historical hdDRSs that included predefined and empirical outcome predictors with dimension reduction (principal component analysis; PCA) and shrinkage (lasso and ridge regression) approaches had higher c-statistics (0.66 for the PCA model, 0.64 for the PCA + ridge and 0.65 for the PCA + lasso models in the warfarin users) than an unreduced model (c-statistic, 0.54) in the dabigatran example. The odds ratio (OR) from PCA + lasso hdDRS-stratification [OR, 0.64; 95 % confidence interval (CI) 0.46–0.90] was closer to the benchmark estimate (0.93) from a randomized trial than the model without empirical predictors (OR, 0.58; 95 % CI 0.41–0.81). In the coxibs example, c-statistics of the hdDRSs in the nsNSAID initiators were 0.66 for the PCA model, 0.67 for the PCA + ridge model, and 0.67 for the PCA + lasso model; these were higher than for the unreduced model (c-statistic, 0.45), and comparable to the demographics + risk score model (c-statistic, 0.67). Conclusions: hdDRSs using historical data with dimension reduction and shrinkage was feasible, and improved confounding adjustment in two studies of newly marketed medications. Electronic supplementary material The online version of this article (doi:10.1186/s12982-016-0047-x) contains supplementary material, which is available to authorized users.
Published Version: doi:10.1186/s12982-016-0047-x
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4822311/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:26860298
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