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Activity Number: 59 - Evidence-Generation via Big Data in the Real-World Setting
Type: Topic Contributed
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: Stats. Partnerships Among Academe, Indust. & Govt. Committee
Abstract #324994
Title: Combining Clinical and Nonclinical Big Data from Multiple Sources
Author(s): Javier Cabrera* and Birol Emir and Demissie Alemayehu
Companies: Rutgers University and Pfizer Inc and Pfizer Inc
Keywords: Variable weights ; LASSO ; GLMNET ; GWAS ; rnaseq

One of the sources of big data in medicine is when clinical data is paired with other sources of data, for example blood samples are used to obtain GWAS, gene expression, rnaSeq data. In addition Healthcare provider databases include real world patient data that contains information of the same disease that in many cases complements the clinical data. We propose new statistical methodology to help combine and analyze such big data. We propose a scheme of weights attached to the variables, not the observations, that helps combine the different sources of data in a more reasonable way. Our scheme is adaptable to penalized methods such Lasso or Glmnet.

Authors who are presenting talks have a * after their name.

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