Warning: Undefined array key "mm" in /www/wwwroot/www.ai-bt.com/si.php on line 10 Deprecated: trim(): Passing null to parameter #1 ($string) of type string is deprecated in /www/wwwroot/www.ai-bt.com/si.php on line 10 Analyzing Selection Bias for Credible Causal Inference: When in Doubt, DAG It Out.

Literature DB >> 31033691

Analyzing Selection Bias for Credible Causal Inference: When in Doubt, DAG It Out.

Onyebuchi A Arah^1,2,3,4,5.

Abstract

Entities: Chemical Disease Gene Species

Mesh：

Year: 2019 PMID： 31033691 PMCID： PMC6597247 DOI： 10.1097/EDE.0000000000001033

Source DB: PubMed Journal: Epidemiology ISSN： 1044-3983 Impact factor: 4.822

× No keyword cloud information.

14 in total

1. Quantifying biases in causal models: classical confounding vs collider-stratification bias.

Authors: Sander Greenland
Journal: Epidemiology Date: 2003-05 Impact factor: 4.822

2. Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders.

Authors: Onyebuchi A Arah; Yasutaka Chiba; Sander Greenland
Journal: Ann Epidemiol Date: 2008-08 Impact factor: 3.797

3. Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods.

Authors: Sander Greenland
Journal: Int J Epidemiol Date: 2009-09-09 Impact factor: 7.196

4. Causal inference and the data-fusion problem.

Authors: Elias Bareinboim; Judea Pearl
Journal: Proc Natl Acad Sci U S A Date: 2016-07-05 Impact factor: 11.205

Review 5. 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

6. Good practices for quantitative bias analysis.

Authors: Timothy L Lash; Matthew P Fox; Richard F MacLehose; George Maldonado; Lawrence C McCandless; Sander Greenland
Journal: Int J Epidemiol Date: 2014-07-30 Impact factor: 7.196

Review 7. Bias from conditioning on live birth in pregnancy cohorts: an illustration based on neurodevelopment in children after prenatal exposure to organic pollutants.

Authors: Zeyan Liew; Jørn Olsen; Xin Cui; Beate Ritz; Onyebuchi A Arah
Journal: Int J Epidemiol Date: 2015-01-19 Impact factor: 7.196

Analyzing Selection Bias for Credible Causal Inference: When in Doubt, DAG It Out.

1. Quantifying biases in causal models: classical confounding vs collider-stratification bias.

2. Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders.

3. Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods.

4. Causal inference and the data-fusion problem.

Review 5. Bias Analysis for Uncontrolled Confounding in the Health Sciences.

6. Good practices for quantitative bias analysis.

Review 7. Bias from conditioning on live birth in pregnancy cohorts: an illustration based on neurodevelopment in children after prenatal exposure to organic pollutants.

8. Competing risk bias to explain the inverse relationship between smoking and malignant melanoma.

9. Selection bias modeling using observed data augmented with imputed record-level probabilities.

10. Sensitivity Analysis Without Assumptions.

1. Invited Commentary: Making Causal Inference More Social and (Social) Epidemiology More Causal.