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Activity Number: 208 - Personalized and Precision Medicine
Type: Contributed
Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
Sponsor: Biometrics Section
Abstract #318104
Title: Statistics Methods for Assessing Drug Interactions Using Observational Data
Author(s): Qian Xu* and Maiying Kong
Companies: University of Louisville and University of Louisville
Keywords: Drug interaction; Propensity score weighting; Marginal structural models; Average treatment effect
Abstract:

With the advance of medicine, many drugs/treatments become available to treat patients. On the one hand, combination treatments have been developed to treat severe diseases such as cancer. On the other hand, polydrug use (i.e., using more than one drug at a time) may cause severe side effect. The observational data such as electronic health records may provide very useful information for drug interactions. In this article we develop statistical methods for evaluating the causal effect and causal interactions of two drugs. In particular, we propose a marginal structural model to assess the causal interaction of two drugs by controlling confounding variables. The joint effect of the two treatments are assessed using the weighted likelihood approach with weights being the inverse probability of treatment assigned. Simulation studies were conducted to examine the performances of the proposed method. Case study was conducted to investigate the joint effect of antecedent statins and opioids use on biomarkers for COVID-19 hospitalized patients.


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

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