ArticleStatistical methods in medical research2019
Causal inference with multiple concurrent medications: A comparison of methods and an application in multidrug-resistant tuberculosis.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patientdata meta-analysis.Statistical methods in medical research · 2022Pooled it
- CausalMetaR: An R package for performing causally interpretable meta-analyses.Research synthesis methods · 2025Review
- Targeted learning in observational studies with multi-valued treatments: An evaluation of antipsychotic drug treatment safety.Statistics in medicine · 2024Article
- Comparative effectiveness of adding delamanid to a multidrug-resistant tuberculosis regimen comprised of three drugs likely to be effective.PLOS global public health · 2023Article
- Estimating the effect of a rifampicin resistant tuberculosis diagnosis by the Xpert MTB/RIF assay on two-year mortality.PLOS global public health · 2023Article
- Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification.Journal of applied statistics · 2022Article
- Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization.BMC bioinformatics · 2021Article
- Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases.Frontiers in chemistry · 2021Review
- EA3: A softmax algorithm for evidence appraisal aggregation.PloS one · 2021Article
Corrections and comments
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Authors and funding
11 authors at 8 institutions in 4 countries.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.