An introduction to causal inference for pharmacometricians

被引：11

作者：

Rogers, James A. A. ^{[1
]}

Maas, Hugo ^{[2
]}

Pitarch, Alejandro Perez ^{[2
]}

机构：

[1] Metrum Res Grp, 2 Tunxis Rd, Suite 112, Tariffville, CT 06081 USA

[2] Boehringer Ingelheim Pharm GmbH & Co KG, Ingelheim, Germany

来源：

CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY | 2023年 / 12卷 / 01期

关键词：

TO-TREAT ANALYSIS; G-COMPUTATION; REAL-WORLD; DIAGRAMS; BIAS;

D O I：

10.1002/psp4.12894

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

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).

引用

页码：27 / 40

页数：14

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