Statistical methods for assessing drug interactions using observational data

Abstract

With advances in medicine, many drugs and treatments become available. On the one hand, polydrug use (i.e. using more than one drug at a time) has been used to treat patients with multiple morbid conditions, and polydrug use may cause severe side effects. On the other hand, combination treatments have been successfully developed to treat severe diseases such as cancer and chronic diseases. Observational data, such as electronic health record data, may provide useful information for assessing drug interactions. In this article, we propose using marginal structural models to assess the average treatment effect and causal interaction of two drugs by controlling confounding variables. The causal effect and the interaction of two drugs are assessed using the weighted likelihood approach, with weights being the inverse probability of the treatment assigned. Simulation studies were conducted to examine the performance of the proposed method, which showed that the proposed method was able to estimate the causal parameters consistently. Case studies were conducted to examine the joint effect of metformin and glyburide use on reducing the hospital readmission for type 2 diabetic patients, and to examine the joint effect of antecedent statins and opioids use on the immune and inflammatory biomarkers for COVID-19 hospitalized patients.

Keywords:

• Drug interaction
• generalized propensity score
• marginal structural models
• multinomial logistic regression

Acknowledgments

The authors thank the University of Louisville Center of Excellence for Research in Infectious Diseases (CERID) group for providing the COVID-19 data set for the study of opioids and statins. The authors would like to thank the Associate Editor and three anonymous reviewers for their constructive comments which led to this improved version.

Disclosure statement

No potential conflict of interest was reported by the author(s).