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Literature DB >> 27682524

Commentary: Multiple Causes of Death: The Importance of Substantive Knowledge in the Big Data Era.

Sebastien Haneuse¹.

Abstract

Entities: Disease Species

Mesh：

Year: 2017 PMID： 27682524 PMCID： PMC5130590 DOI： 10.1097/EDE.0000000000000566

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

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15 in total

1. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology.

Authors: Miguel A Hernán; Sonia Hernández-Díaz; Martha M Werler; Allen A Mitchell
Journal: Am J Epidemiol Date: 2002-01-15 Impact factor: 4.897

2. Bayesian effect estimation accounting for adjustment uncertainty.

Authors: Chi Wang; Giovanni Parmigiani; Francesca Dominici
Journal: Biometrics Date: 2012-02-24 Impact factor: 2.571

3. Role of electronic health records in comparative effectiveness research.

Authors: Blanca Gallego; Adam G Dunn; Enrico Coiera
Journal: J Comp Eff Res Date: 2013-11 Impact factor: 1.744

4. Bayesian Semi-parametric Analysis of Semi-competing Risks Data: Investigating Hospital Readmission after a Pancreatic Cancer Diagnosis.

Authors: Kyu Ha Lee; Sebastien Haneuse; Deborah Schrag; Francesca Dominici
Journal: J R Stat Soc Ser C Appl Stat Date: 2015-02-01 Impact factor: 1.864

10. Semi-Competing Risks Data Analysis: Accounting for Death as a Competing Risk When the Outcome of Interest Is Nonterminal.

Authors: Sebastien Haneuse; Kyu Ha Lee
Journal: Circ Cardiovasc Qual Outcomes Date: 2016-04-12

Commentary: Multiple Causes of Death: The Importance of Substantive Knowledge in the Big Data Era.

1. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology.

2. Bayesian effect estimation accounting for adjustment uncertainty.

3. Role of electronic health records in comparative effectiveness research.

4. Bayesian Semi-parametric Analysis of Semi-competing Risks Data: Investigating Hospital Readmission after a Pancreatic Cancer Diagnosis.

5. Survival Analysis with Multiple Causes of Death: Extending the Competing Risks Model.

6. Bias Due to Confounders for the Exposure-Competing Risk Relationship.

7. A Simple Regression-based Approach to Account for Survival Bias in Birth Outcomes Research.

8. Competing risk regression models for epidemiologic data.

9. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

10. Semi-Competing Risks Data Analysis: Accounting for Death as a Competing Risk When the Outcome of Interest Is Nonterminal.