Directed acyclic graphs and causal thinking in clinical risk prediction modeling-Reference-Cited by-同舟云学术

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Directed acyclic graphs and causal thinking in clinical risk prediction modeling

Published:2020-07-02 Issue:1 Volume:20 Page:
ISSN:1471-2288
Container-title:BMC Medical Research Methodology
language:en
Short-container-title:BMC Med Res Methodol

Author:

Piccininni Marco^ORCID,Konigorski Stefan,Rohmann Jessica L.,Kurth Tobias

Funder

Else Kröner-Fresenius-Stiftung

Deutsche Forschungsgemeinschaft

Charité – Universitätsmedizin Berlin

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Epidemiology

Link

http://link.springer.com/content/pdf/10.1186/s12874-020-01058-z.pdf

Reference24 articles.

1. Peters J, Janzing D, Schölkopf B. Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge: MIT Press; 2017.

2. Sperrin M, Martin GP, Pate A, Van Staa T, Peek N, Buchan I. Using marginal structural models to adjust for treatment drop-in when developing clinical prediction models. Stat Med. 2018;37:4142–54. https://doi.org/10.1002/sim.7913 .

3. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37–48.

4. Greenland S, Pearl J. Causal Diagrams: Wiley StatsRef. Statistics Reference Online; 2014. https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118445112.stat03732 .

5. Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol. 2002;155:176–84.

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