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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2020
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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. |
format | Online Article Text |
id | pubmed-8823604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
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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