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Literature DB >> 25265135

Commentary: Applying a causal road map in settings with time-dependent confounding.

Maya L Petersen¹.

Abstract

Entities: Disease Gene Species

Mesh：

Year: 2014 PMID： 25265135 PMCID： PMC4460577 DOI： 10.1097/EDE.0000000000000178

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

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11 in total

1. Marginal structural models and causal inference in epidemiology.

Authors: J M Robins; M A Hernán; B Brumback
Journal: Epidemiology Date: 2000-09 Impact factor: 4.822

2. Using inverse weighting and predictive inference to estimate the effects of time-varying treatments on the discrete-time hazard.

Authors: Ree Dawson; Philip W Lavori
Journal: Stat Med Date: 2002-06-30 Impact factor: 2.373

3. A structural approach to selection bias.

Authors: Miguel A Hernán; Sonia Hernández-Díaz; James M Robins
Journal: Epidemiology Date: 2004-09 Impact factor: 4.822

4. Doubly robust estimation in missing data and causal inference models.

Authors: Heejung Bang; James M Robins
Journal: Biometrics Date: 2005-12 Impact factor: 2.571

5. Super learner.

Authors: Mark J van der Laan; Eric C Polley; Alan E Hubbard
Journal: Stat Appl Genet Mol Biol Date: 2007-09-16

6. Intervening on risk factors for coronary heart disease: an application of the parametric g-formula.

Authors: Sarah L Taubman; James M Robins; Murray A Mittleman; Miguel A Hernán
Journal: Int J Epidemiol Date: 2009-04-23 Impact factor: 7.196

7. Targeted minimum loss based estimation of causal effects of multiple time point interventions.

Authors: Mark J van der Laan; Susan Gruber
Journal: Int J Biostat Date: 2012 Impact factor: 0.968

8. Invited commentary: composite outcomes as an attempt to escape from selection bias and related paradoxes.

Authors: Miguel A Hernán; Enrique F Schisterman; Sonia Hernández-Díaz
Journal: Am J Epidemiol Date: 2013-11-27 Impact factor: 4.897

9. The parametric g-formula for time-to-event data: intuition and a worked example.

Authors: Alexander P Keil; Jessie K Edwards; David B Richardson; Ashley I Naimi; Stephen R Cole
Journal: Epidemiology Date: 2014-11 Impact factor: 4.822

10. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease.

Authors: Miguel A Hernán; Alvaro Alonso; Roger Logan; Francine Grodstein; Karin B Michels; Walter C Willett; Joann E Manson; James M Robins
Journal: Epidemiology Date: 2008-11 Impact factor: 4.822

9 in total

1. Evaluating the Impact of a HIV Low-Risk Express Care Task-Shifting Program: A Case Study of the Targeted Learning Roadmap.

Authors: Linh Tran; Constantin T Yiannoutsos; Beverly S Musick; Kara K Wools-Kaloustian; Abraham Siika; Sylvester Kimaiyo; Mark J van der Laan; Maya Petersen
Journal: Epidemiol Methods Date: 2016-11-10

9. Estimating the Comparative Effectiveness of Feeding Interventions in the Pediatric Intensive Care Unit: A Demonstration of Longitudinal Targeted Maximum Likelihood Estimation.

Authors: Noémi Kreif; Linh Tran; Richard Grieve; Bianca De Stavola; Robert C Tasker; Maya Petersen
Journal: Am J Epidemiol Date: 2017-12-15 Impact factor: 5.363

9 in total

Commentary: Applying a causal road map in settings with time-dependent confounding.

1. Marginal structural models and causal inference in epidemiology.

2. Using inverse weighting and predictive inference to estimate the effects of time-varying treatments on the discrete-time hazard.

3. A structural approach to selection bias.

4. Doubly robust estimation in missing data and causal inference models.

5. Super learner.

6. Intervening on risk factors for coronary heart disease: an application of the parametric g-formula.

7. Targeted minimum loss based estimation of causal effects of multiple time point interventions.

8. Invited commentary: composite outcomes as an attempt to escape from selection bias and related paradoxes.

9. The parametric g-formula for time-to-event data: intuition and a worked example.

10. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease.

1. Evaluating the Impact of a HIV Low-Risk Express Care Task-Shifting Program: A Case Study of the Targeted Learning Roadmap.

2. The Epidemiologic Toolbox: Identifying, Honing, and Using the Right Tools for the Job.

3. The Critical Importance of Asking Good Questions: The Role of Epidemiology Doctoral Training Programs.

4. Bootstrap inference when using multiple imputation.

5. Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions.

6. The Role of the Natural Course in Causal Analysis.

7. Counterpoint: Keeping the Demons at Bay When Handling Time-Varying Exposures-Beyond Avoiding Immortal Person-Time.

8. Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science.

9. Estimating the Comparative Effectiveness of Feeding Interventions in the Pediatric Intensive Care Unit: A Demonstration of Longitudinal Targeted Maximum Likelihood Estimation.