Causal inference with multiple concurrent medications: A comparison of methods and an application in multidrug-resistant tuberculosis

Stat Methods Med Res. 2019 Dec;28(12):3534-3549. doi: 10.1177/0962280218808817. Epub 2018 Oct 31.


This paper investigates different approaches for causal estimation under multiple concurrent medications. Our parameter of interest is the marginal mean counterfactual outcome under different combinations of medications. We explore parametric and non-parametric methods to estimate the generalized propensity score. We then apply three causal estimation approaches (inverse probability of treatment weighting, propensity score adjustment, and targeted maximum likelihood estimation) to estimate the causal parameter of interest. Focusing on the estimation of the expected outcome under the most prevalent regimens, we compare the results obtained using these methods in a simulation study with four potentially concurrent medications. We perform a second simulation study in which some combinations of medications may occur rarely or not occur at all in the dataset. Finally, we apply the methods explored to contrast the probability of patient treatment success for the most prevalent regimens of antimicrobial agents for patients with multidrug-resistant pulmonary tuberculosis.

Keywords: Causal inference; concurrent medications; generalized propensity score; machine learning; multidrug-resistant tuberculosis; targeted maximum likelihood estimation.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Causality
  • Drug-Related Side Effects and Adverse Reactions
  • Humans
  • Likelihood Functions
  • Machine Learning
  • Models, Statistical
  • Polypharmacology*
  • Propensity Score
  • Regression Analysis
  • Treatment Outcome
  • Tuberculosis, Multidrug-Resistant*