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How to control confounding effects by statistical analysis
A Confounder is a variable whose presence affects the variables being studied so that the results do not reflect the actual relationship. There are various ways to exclude or control confounding variables including Randomization, Restriction and Matching. But all these methods are applicable at the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Institute for Gastroenterology and Liver Diseases
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/ https://www.ncbi.nlm.nih.gov/pubmed/24834204 |
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author | Pourhoseingholi, Mohamad Amin Baghestani, Ahmad Reza Vahedi, Mohsen |
author_facet | Pourhoseingholi, Mohamad Amin Baghestani, Ahmad Reza Vahedi, Mohsen |
author_sort | Pourhoseingholi, Mohamad Amin |
collection | PubMed |
description | A Confounder is a variable whose presence affects the variables being studied so that the results do not reflect the actual relationship. There are various ways to exclude or control confounding variables including Randomization, Restriction and Matching. But all these methods are applicable at the time of study design. When experimental designs are premature, impractical, or impossible, researchers must rely on statistical methods to adjust for potentially confounding effects. These Statistical models (especially regression models) are flexible to eliminate the effects of confounders. |
format | Online Article Text |
id | pubmed-4017459 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Research Institute for Gastroenterology and Liver Diseases |
record_format | MEDLINE/PubMed |
spelling | pubmed-40174592014-05-15 How to control confounding effects by statistical analysis Pourhoseingholi, Mohamad Amin Baghestani, Ahmad Reza Vahedi, Mohsen Gastroenterol Hepatol Bed Bench Medical Education A Confounder is a variable whose presence affects the variables being studied so that the results do not reflect the actual relationship. There are various ways to exclude or control confounding variables including Randomization, Restriction and Matching. But all these methods are applicable at the time of study design. When experimental designs are premature, impractical, or impossible, researchers must rely on statistical methods to adjust for potentially confounding effects. These Statistical models (especially regression models) are flexible to eliminate the effects of confounders. Research Institute for Gastroenterology and Liver Diseases 2012 /pmc/articles/PMC4017459/ /pubmed/24834204 Text en Copyright © 2012 Research Institute for Gastroenterology and Liver Diseases http://creativecommons.org/licenses/by-nc/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly. |
spellingShingle | Medical Education Pourhoseingholi, Mohamad Amin Baghestani, Ahmad Reza Vahedi, Mohsen How to control confounding effects by statistical analysis |
title | How to control confounding effects by statistical analysis |
title_full | How to control confounding effects by statistical analysis |
title_fullStr | How to control confounding effects by statistical analysis |
title_full_unstemmed | How to control confounding effects by statistical analysis |
title_short | How to control confounding effects by statistical analysis |
title_sort | how to control confounding effects by statistical analysis |
topic | Medical Education |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/ https://www.ncbi.nlm.nih.gov/pubmed/24834204 |
work_keys_str_mv | AT pourhoseingholimohamadamin howtocontrolconfoundingeffectsbystatisticalanalysis AT baghestaniahmadreza howtocontrolconfoundingeffectsbystatisticalanalysis AT vahedimohsen howtocontrolconfoundingeffectsbystatisticalanalysis |