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4099 Principles of Statistical Education for Translational Scientists in the Age of Rigor, Reproducibility, and Reporting

OBJECTIVES/GOALS: To describe principles, best practices, and techniques recommended to instill deep understanding of the application and interpretation of statistical techniques and statistical inference among translational scientists and trainees, that best support the concepts of scientific Rigor...

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Autores principales: Bagiella, Emilia, Christos, Paul, Kim, Mimi, Lee, Shing, Vaughan, Roger, Zhong, Judy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823604/
http://dx.doi.org/10.1017/cts.2020.183
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author Bagiella, Emilia
Christos, Paul
Kim, Mimi
Lee, Shing
Vaughan, Roger
Zhong, Judy
author_facet Bagiella, Emilia
Christos, Paul
Kim, Mimi
Lee, Shing
Vaughan, Roger
Zhong, Judy
author_sort Bagiella, Emilia
collection PubMed
description OBJECTIVES/GOALS: To describe principles, best practices, and techniques recommended to instill deep understanding of the application and interpretation of statistical techniques and statistical inference among translational scientists and trainees, that best support the concepts of scientific Rigor, Reproducibility and Reporting. METHODS/STUDY POPULATION: Each of the six New York City Area Biostatistics, Epidemiology and Research Design (BERD) resources have strong educational programs, novel curricular components, and creative strategies, implemented by award winning educators. To capitalize on shared knowledge, innovation, and resources, the six teams formed the New York City Area BERD Collaborative (NYC-ABC) Value of team science approach and including biostatisticians early and often. Carefully designing experiments to reduce bias and increase precision. Trainees’ focus is often on “statistical significance” and the p-value. Consequences of data dredging/p-hacking, and the impact of sample size and other factors on statistical significance. Emphasizing the effect size and answering the scientific hypothesis when reporting results. Statistical code used to produce results should be well annotated and raw data posted online to enhance reproducibility. Incorporate effective multiple modalities (i.e. didactic, demonstrative, hands on workshops, applications, and tools). Approach from “the drivers’ seat” perspective, rather than strictly mathematical. Endorse flipped classroom approach. DISCUSSION/SIGNIFICANCE OF IMPACT: Like any complex discipline, biostatistical education can be approached from several dimensions, but it remains essential to focus on fundamental goals of science. We remind our trainees that the goal of science is to create knowledge, not to “find significance”. Deep understanding of inferential methods and proper interpretation of results are key. CONFLICT OF INTEREST DESCRIPTION: None.
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spelling pubmed-88236042022-02-18 4099 Principles of Statistical Education for Translational Scientists in the Age of Rigor, Reproducibility, and Reporting Bagiella, Emilia Christos, Paul Kim, Mimi Lee, Shing Vaughan, Roger Zhong, Judy J Clin Transl Sci Data Science/Biostatistics/Informatics OBJECTIVES/GOALS: To describe principles, best practices, and techniques recommended to instill deep understanding of the application and interpretation of statistical techniques and statistical inference among translational scientists and trainees, that best support the concepts of scientific Rigor, Reproducibility and Reporting. METHODS/STUDY POPULATION: Each of the six New York City Area Biostatistics, Epidemiology and Research Design (BERD) resources have strong educational programs, novel curricular components, and creative strategies, implemented by award winning educators. To capitalize on shared knowledge, innovation, and resources, the six teams formed the New York City Area BERD Collaborative (NYC-ABC) Value of team science approach and including biostatisticians early and often. Carefully designing experiments to reduce bias and increase precision. Trainees’ focus is often on “statistical significance” and the p-value. Consequences of data dredging/p-hacking, and the impact of sample size and other factors on statistical significance. Emphasizing the effect size and answering the scientific hypothesis when reporting results. Statistical code used to produce results should be well annotated and raw data posted online to enhance reproducibility. Incorporate effective multiple modalities (i.e. didactic, demonstrative, hands on workshops, applications, and tools). Approach from “the drivers’ seat” perspective, rather than strictly mathematical. Endorse flipped classroom approach. DISCUSSION/SIGNIFICANCE OF IMPACT: Like any complex discipline, biostatistical education can be approached from several dimensions, but it remains essential to focus on fundamental goals of science. We remind our trainees that the goal of science is to create knowledge, not to “find significance”. Deep understanding of inferential methods and proper interpretation of results are key. CONFLICT OF INTEREST DESCRIPTION: None. Cambridge University Press 2020-07-29 /pmc/articles/PMC8823604/ http://dx.doi.org/10.1017/cts.2020.183 Text en © The Association for Clinical and Translational Science 2020 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Data Science/Biostatistics/Informatics
Bagiella, Emilia
Christos, Paul
Kim, Mimi
Lee, Shing
Vaughan, Roger
Zhong, Judy
4099 Principles of Statistical Education for Translational Scientists in the Age of Rigor, Reproducibility, and Reporting
title 4099 Principles of Statistical Education for Translational Scientists in the Age of Rigor, Reproducibility, and Reporting
title_full 4099 Principles of Statistical Education for Translational Scientists in the Age of Rigor, Reproducibility, and Reporting
title_fullStr 4099 Principles of Statistical Education for Translational Scientists in the Age of Rigor, Reproducibility, and Reporting
title_full_unstemmed 4099 Principles of Statistical Education for Translational Scientists in the Age of Rigor, Reproducibility, and Reporting
title_short 4099 Principles of Statistical Education for Translational Scientists in the Age of Rigor, Reproducibility, and Reporting
title_sort 4099 principles of statistical education for translational scientists in the age of rigor, reproducibility, and reporting
topic Data Science/Biostatistics/Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823604/
http://dx.doi.org/10.1017/cts.2020.183
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