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Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data
BACKGROUND: Different study designs and population size may require different sample size for logistic regression. This study aims to propose sample size guidelines for logistic regression based on observational studies with large population. METHODS: We estimated the minimum sample size required ba...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Penerbit Universiti Sains Malaysia
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6422534/ https://www.ncbi.nlm.nih.gov/pubmed/30914854 http://dx.doi.org/10.21315/mjms2018.25.4.12 |
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author | Bujang, Mohamad Adam Sa’at, Nadiah Sidik, Tg Mohd Ikhwan Tg Abu Bakar Joo, Lim Chien |
author_facet | Bujang, Mohamad Adam Sa’at, Nadiah Sidik, Tg Mohd Ikhwan Tg Abu Bakar Joo, Lim Chien |
author_sort | Bujang, Mohamad Adam |
collection | PubMed |
description | BACKGROUND: Different study designs and population size may require different sample size for logistic regression. This study aims to propose sample size guidelines for logistic regression based on observational studies with large population. METHODS: We estimated the minimum sample size required based on evaluation from real clinical data to evaluate the accuracy between statistics derived and the actual parameters. Nagelkerke r-squared and coefficients derived were compared with their respective parameters. RESULTS: With a minimum sample size of 500, results showed that the differences between the sample estimates and the population was sufficiently small. Based on an audit from a medium size of population, the differences were within ± 0.5 for coefficients and ± 0.02 for Nagelkerke r-squared. Meanwhile for large population, the differences are within ± 1.0 for coefficients and ± 0.02 for Nagelkerke r-squared. CONCLUSIONS: For observational studies with large population size that involve logistic regression in the analysis, taking a minimum sample size of 500 is necessary to derive the statistics that represent the parameters. The other recommended rules of thumb are EPV of 50 and formula; n = 100 + 50i where i refers to number of independent variables in the final model. |
format | Online Article Text |
id | pubmed-6422534 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Penerbit Universiti Sains Malaysia |
record_format | MEDLINE/PubMed |
spelling | pubmed-64225342019-03-26 Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data Bujang, Mohamad Adam Sa’at, Nadiah Sidik, Tg Mohd Ikhwan Tg Abu Bakar Joo, Lim Chien Malays J Med Sci Original Article BACKGROUND: Different study designs and population size may require different sample size for logistic regression. This study aims to propose sample size guidelines for logistic regression based on observational studies with large population. METHODS: We estimated the minimum sample size required based on evaluation from real clinical data to evaluate the accuracy between statistics derived and the actual parameters. Nagelkerke r-squared and coefficients derived were compared with their respective parameters. RESULTS: With a minimum sample size of 500, results showed that the differences between the sample estimates and the population was sufficiently small. Based on an audit from a medium size of population, the differences were within ± 0.5 for coefficients and ± 0.02 for Nagelkerke r-squared. Meanwhile for large population, the differences are within ± 1.0 for coefficients and ± 0.02 for Nagelkerke r-squared. CONCLUSIONS: For observational studies with large population size that involve logistic regression in the analysis, taking a minimum sample size of 500 is necessary to derive the statistics that represent the parameters. The other recommended rules of thumb are EPV of 50 and formula; n = 100 + 50i where i refers to number of independent variables in the final model. Penerbit Universiti Sains Malaysia 2018-07 2018-08-30 /pmc/articles/PMC6422534/ /pubmed/30914854 http://dx.doi.org/10.21315/mjms2018.25.4.12 Text en © Penerbit Universiti Sains Malaysia, 2018 This work is licensed under the terms of the Creative Commons Attribution (CC BY) (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Original Article Bujang, Mohamad Adam Sa’at, Nadiah Sidik, Tg Mohd Ikhwan Tg Abu Bakar Joo, Lim Chien Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data |
title | Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data |
title_full | Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data |
title_fullStr | Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data |
title_full_unstemmed | Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data |
title_short | Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data |
title_sort | sample size guidelines for logistic regression from observational studies with large population: emphasis on the accuracy between statistics and parameters based on real life clinical data |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6422534/ https://www.ncbi.nlm.nih.gov/pubmed/30914854 http://dx.doi.org/10.21315/mjms2018.25.4.12 |
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