Literature DB >> 31595956

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

Matthew P Fox1,2, Jessie K Edwards3, Robert Platt4,5, Laura B Balzer6.   

Abstract

Epidemiologic methods have advanced tremendously in the last several decades. As important as they are, even the most sophisticated approaches are unable to provide meaningful answers when the user lacks a clear study question. Yet, instructors have more and more resources on how to conduct studies and analyze data but few resources on how to ask clearly defined study questions that will guide those methods. Training programs have limited time for coursework, and if novel statistical estimation methods become the focus of instruction, programs that go this route may end up underemphasizing the process of asking good study questions, designing robust studies, considering potential biases in the collected data, and appropriately interpreting the results of the analysis. Given the demands for space in curricula, now is an appropriate time to reevaluate what we teach epidemiology doctoral students. We advocate that programs place a renewed focus on asking good study questions and following a comprehensive approach to study design and data analysis in which questions guide the choice of appropriate methods, helping us avoid methods for methods' sake and highlighting when application of a new method can provide the opportunity to answer questions that were intractable with traditional approaches.
© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Keywords:  causal inference; epidemiologic methods; novel methods; study questions; teaching; training

Mesh:

Year:  2020        PMID: 31595956      PMCID: PMC7305787          DOI: 10.1093/aje/kwz233

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  17 in total

1.  A method to automate probabilistic sensitivity analyses of misclassified binary variables.

Authors:  Matthew P Fox; Timothy L Lash; Sander Greenland
Journal:  Int J Epidemiol       Date:  2005-09-19       Impact factor: 7.196

2.  The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials.

Authors:  Donald B Rubin
Journal:  Stat Med       Date:  2007-01-15       Impact factor: 2.373

3.  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

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

Authors:  Maya L Petersen
Journal:  Epidemiology       Date:  2014-11       Impact factor: 4.822

5.  The Future of Teaching Epidemiology.

Authors:  Martha M Werler; Sherri O Stuver; Megan A Healey; Wayne W LaMorte
Journal:  Am J Epidemiol       Date:  2019-05-01       Impact factor: 4.897

6.  The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data.

Authors:  Miguel A Hernán
Journal:  Am J Public Health       Date:  2018-03-22       Impact factor: 9.308

7.  Start With the "C-Word," Follow the Roadmap for Causal Inference.

Authors:  Jennifer Ahern
Journal:  Am J Public Health       Date:  2018-05       Impact factor: 9.308

Review 8.  Causal models and learning from data: integrating causal modeling and statistical estimation.

Authors:  Maya L Petersen; Mark J van der Laan
Journal:  Epidemiology       Date:  2014-05       Impact factor: 4.822

9.  Emulating a target trial of antiretroviral therapy regimens started before conception and risk of adverse birth outcomes.

Authors:  Ellen C Caniglia; Rebecca Zash; Denise L Jacobson; Modiegi Diseko; Gloria Mayondi; Shahin Lockman; Jennifer Y Chen; Mompati Mmalane; Joseph Makhema; Miguel A Hernán; Roger L Shapiro
Journal:  AIDS       Date:  2018-01-02       Impact factor: 4.177

10.  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

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  4 in total

1.  Is the Way Forward to Step Back? Documenting the Frequency With Which Study Goals Are Misaligned With Study Methods and Interpretations in the Epidemiologic Literature.

Authors:  Katrina L Kezios
Journal:  Epidemiol Rev       Date:  2022-01-14       Impact factor: 4.280

2.  Thirteen Questions About Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!).

Authors:  Stephen J Mooney; Alexander P Keil; Daniel J Westreich
Journal:  Am J Epidemiol       Date:  2021-08-01       Impact factor: 4.897

3.  Defining Core Competencies for Epidemiologists in Academic Settings to Tackle Tomorrow's Health Research Challenges: A Structured, Multinational Effort.

Authors:  Alison Abraham; Doreen Gille; Milo A Puhan; Gerben Ter Riet; Viktor von Wyl
Journal:  Am J Epidemiol       Date:  2021-02-01       Impact factor: 4.897

4.  On the Need to Revitalize Descriptive Epidemiology.

Authors:  Matthew P Fox; Eleanor J Murray; Catherine R Lesko; Shawnita Sealy-Jefferson
Journal:  Am J Epidemiol       Date:  2022-06-27       Impact factor: 5.363

  4 in total

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