Literature DB >> 33083814

Simulation as a Tool for Teaching and Learning Epidemiologic Methods.

Jacqueline E Rudolph, Matthew P Fox, Ashley I Naimi.   

Abstract

In aspiring to be discerning epidemiologists, we must learn to think critically about the fundamental concepts in our field and be able to understand and apply many of the novel methods being developed today. We must also find effective ways to teach both basic and advanced topics in epidemiology to graduate students, in a manner that goes beyond simple provision of knowledge. Here, we argue that simulation is one critical tool that can be used to help meet these goals, by providing examples of how simulation can be used to address 2 common misconceptions in epidemiology. First, we show how simulation can be used to explore nondifferential exposure misclassification. Second, we show how an instructor could use simulation to provide greater clarity on the correct definition of the P value. Through these 2 examples, we highlight how simulation can be used to both clearly and concretely demonstrate theoretical concepts, as well as to test and experiment with ideas, theories, and methods in a controlled environment. Simulation is therefore useful not only in the classroom but also as a skill for independent self-learning.
© The Author(s) 2020. 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:  zzm321990 P value; dependent misclassification; education; nondifferential misclassification; simulation

Year:  2021        PMID: 33083814      PMCID: PMC8096491          DOI: 10.1093/aje/kwaa232

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


  7 in total

1.  Proper interpretation of non-differential misclassification effects: expectations vs observations.

Authors:  Anne M Jurek; Sander Greenland; George Maldonado; Timothy R Church
Journal:  Int J Epidemiol       Date:  2005-03-31       Impact factor: 7.196

2.  The Monte Carlo method.

Authors:  N METROPOLIS; S ULAM
Journal:  J Am Stat Assoc       Date:  1949-09       Impact factor: 5.033

3.  Results on differential and dependent measurement error of the exposure and the outcome using signed directed acyclic graphs.

Authors:  Tyler J VanderWeele; Miguel A Hernán
Journal:  Am J Epidemiol       Date:  2012-05-08       Impact factor: 4.897

4.  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

5.  Things Don't Always Go as Expected: The Example of Nondifferential Misclassification of Exposure-Bias and Error.

Authors:  Brian W Whitcomb; Ashley I Naimi
Journal:  Am J Epidemiol       Date:  2020-05-05       Impact factor: 4.897

6.  The design of simulation studies in medical statistics.

Authors:  Andrea Burton; Douglas G Altman; Patrick Royston; Roger L Holder
Journal:  Stat Med       Date:  2006-12-30       Impact factor: 2.373

7.  Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.

Authors:  Sander Greenland; Stephen J Senn; Kenneth J Rothman; John B Carlin; Charles Poole; Steven N Goodman; Douglas G Altman
Journal:  Eur J Epidemiol       Date:  2016-05-21       Impact factor: 8.082

  7 in total

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