Article ; Online: An introduction to causal inference for pharmacometricians.
CPT: pharmacometrics & systems pharmacology
2022 Volume 12, Issue 1, Page(s) 27–40
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 ... ...
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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MeSH term(s) | Humans ; Artificial Intelligence ; Confounding Factors, Epidemiologic ; Data Interpretation, Statistical ; Biometry ; Causality |
Language | English |
Publishing date | 2022-12-08 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 2697010-7 |
ISSN | 2163-8306 ; 2163-8306 |
ISSN (online) | 2163-8306 |
ISSN | 2163-8306 |
DOI | 10.1002/psp4.12894 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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