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Literature DB >> 31083838

Use of Causal Diagrams for Nursing Research: a Tool for Application in Epidemiological Studies.

Wilson Cañón Montañez¹, Alba Luz Rodríguez Acelas².

Abstract

Entities: Chemical Disease Species

Mesh：

Year: 2019 PMID： 31083838 PMCID： PMC7871467 DOI： 10.17533/udea.iee.v37n1e01

Source DB: PubMed Journal: Invest Educ Enferm ISSN： 0120-5307

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References

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8 in total

1. Robust causal inference using directed acyclic graphs: the R package 'dagitty'.

Authors: Johannes Textor; Benito van der Zander; Mark S Gilthorpe; Maciej Liskiewicz; George Th Ellison
Journal: Int J Epidemiol Date: 2016-12-01 Impact factor: 7.196

2. Serum bilirubin and the risk of hypertension.

Authors: Lina Wang; Leonelo E Bautista
Journal: Int J Epidemiol Date: 2014-12-25 Impact factor: 7.196

3. Sensitivity Analysis in Observational Research: Introducing the E-Value.

Authors: Tyler J VanderWeele; Peng Ding
Journal: Ann Intern Med Date: 2017-07-11 Impact factor: 25.391

Review 4. Bias Analysis for Uncontrolled Confounding in the Health Sciences.

Authors: Onyebuchi A Arah
Journal: Annu Rev Public Health Date: 2017-01-06 Impact factor: 21.981

5. Central Obesity is the Key Component in the Association of Metabolic Syndrome With Left Ventricular Global Longitudinal Strain Impairment.

Authors: Wilson Cañon-Montañez; Angela B S Santos; Lisandra A Nunes; Julio C G Pires; Claudia M V Freire; Antonio L P Ribeiro; José G Mill; Marina Bessel; Bruce B Duncan; Maria Inês Schmidt; Murilo Foppa
Journal: Rev Esp Cardiol (Engl Ed) Date: 2017-11-14

Use of Causal Diagrams for Nursing Research: a Tool for Application in Epidemiological Studies.

1. Robust causal inference using directed acyclic graphs: the R package 'dagitty'.

2. Serum bilirubin and the risk of hypertension.

3. Sensitivity Analysis in Observational Research: Introducing the E-Value.

Review 4. Bias Analysis for Uncontrolled Confounding in the Health Sciences.

5. Central Obesity is the Key Component in the Association of Metabolic Syndrome With Left Ventricular Global Longitudinal Strain Impairment.

6. [Use of causal diagrams in Epidemiology: application to a situation with confounding].

7. Causal inference based on counterfactuals.

8. Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms.