Event

Abstract:

For safety and rare outcome studies in pharmacoepidemiology, multiple, large databases are often merged to improve statistical power and create a more generalizable cohort. Medical claims data have become a mainstay in evaluating the safety and effectiveness of medications post-approval, but confounders derived from administrative data can be prone to measurement error. Electronic health records (EHR) data or data abstracted from chart review have more granular patient data than do medical claims, but the gold standard exposure data may only be available on a subset. I will discuss two practical-to-implement doubly-robust estimators for this setting, one relying on a type of survey calibration and another utilizing targeted maximum likelihood estimation (TMLE), and compare their performance with that of more traditional missing data methods in a detailed numerical study. Numerical work includes plasmode simulation studies that emulate the complex data structure of a real large electronic health records cohort in order to compare anti-depressant therapies in a setting where a key confounder is prone to missingness.

Dr. Pamela Shaw is a Senior Investigator inthe Biostatistics Unit of Kaiser Permanente Washington Health Research Institute with expertise in clinical trials, design and analysis of complex epidemiologic studies, measurement error, and survival analysis. Dr. Shaw’s current statistical research includes a focus on methodology and validation study designs to correct for covariate and outcome measurement error, with application to studies reliant on electronic health records and large observational cohort studies. She is co-Director of the Data Analysis Core for the Adult Changes in Thought (ACT) study and has continued collaborations in a variety of epidemiologic and clinical studies, with a focus on chronic and infectious diseases and lifestyle exposures.  

Dr. Shaw also has an interest in graduate education having mentored several graduate students. She is co-author of the textbook Essentials of Probability Theory for Statisticians and is Affiliate Professor at the Department of Biostatistics at the University of Washington and Adjunct Associate Professor in the Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania. She is fellow of the American Statistical Association. She completed a BA in Mathematics and French at Grinnell College, and an MS in Mathematics and Ph.D. in Biostatistics at the University of Washington.

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Meeting ID: 870 1537 8482

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