ArticleCPT: pharmacometrics & systems pharmacology2023
An introduction to causal inference for pharmacometricians.
Article in CPT: pharmacometrics & systems pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Pharmaceutical Industry Perspectives on Exposure-Response Confounding in Large Molecule Therapeutics: Results From an IQ Consortium Survey.CPT: pharmacometrics & systems pharmacology · 2026Article
- Blood pressure thresholds for the administration of balanced crystalloids and the effect on intra-operative hypotension: A proof-of-concept causal inference analysis featuring idealised dynamic treatment regimens.European journal of anaesthesiology · 2026Observational
- Objective First, Method Second: Why the Estimand Definition Comes First in Pharmacometric Covariate Modeling.CPT: pharmacometrics & systems pharmacology · 2026Article
- Causal Directed Acyclic Graphs to Mitigate Confounding Bias in Exposure-Response Analyses.CPT: pharmacometrics & systems pharmacology · 2026Article
- Impact of Event-Related Treatment Modifications in Exposure-Response Analyses of Repeated Time-to-Event Data.CPT: pharmacometrics & systems pharmacology · 2026Article
- Some Common Dose-Exposure-Response Estimands and Conditions for Their Causal Identifiability.CPT: pharmacometrics & systems pharmacology · 2026Article
- Tutorial on Causal Mediation Analysis for Pharmacometricians.CPT: pharmacometrics & systems pharmacology · 2026Article
- Nonlinear mixed-effects modeling as a method for causal inference to predict exposures under desired within-subject dose titration schemes.CPT: pharmacometrics & systems pharmacology · 2025Article
- Population PK modeling of certolizumab pegol in pregnant women with chronic inflammatory diseases.CPT: pharmacometrics & systems pharmacology · 2024Article
- Review
- Covariate modeling in pharmacometrics: General points for consideration.CPT: pharmacometrics & systems pharmacology · 2024Review
- Using forest plots to interpret covariate effects in pharmacometric models.CPT: pharmacometrics & systems pharmacology · 2024Article
- Case-control matching-guided exposure-efficacy relationship for avelumab in patients with urothelial carcinoma.CPT: pharmacometrics & systems pharmacology · 2023Article
- Machine Learning for Exposure-Response Analysis: Methodological Considerations and Confirmation of Their Importance via Computational Experimentations.Pharmaceutics · 2023Article
- An introduction to causal inference for pharmacometricians.CPT: pharmacometrics & systems pharmacology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As formal causal inference begins to play a greater role in disciplines that intersect with pharmacometrics, such as biostatistics, epidemiology, and artificial intelligence/machine learning, pharmacometricians may increasingly benefit from a basic fluency in foundational causal inference concepts. This tutorial seeks to orient pharmacometricians to three such fundamental concepts: potential outcomes, g-formula, and directed acyclic graphs (DAGs).
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