Cargando…

Sample size estimation and power analysis for clinical research studies

Determining the optimal sample size for a study assures an adequate power to detect statistical significance. Hence, it is a critical step in the design of a planned research protocol. Using too many participants in a study is expensive and exposes more number of subjects to procedure. Similarly, if...

Descripción completa

Detalles Bibliográficos
Autores principales: Suresh, KP, Chandrashekara, S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3409926/
https://www.ncbi.nlm.nih.gov/pubmed/22870008
http://dx.doi.org/10.4103/0974-1208.97779
_version_ 1782239642442006528
author Suresh, KP
Chandrashekara, S
author_facet Suresh, KP
Chandrashekara, S
author_sort Suresh, KP
collection PubMed
description Determining the optimal sample size for a study assures an adequate power to detect statistical significance. Hence, it is a critical step in the design of a planned research protocol. Using too many participants in a study is expensive and exposes more number of subjects to procedure. Similarly, if study is underpowered, it will be statistically inconclusive and may make the whole protocol a failure. This paper covers the essentials in calculating power and sample size for a variety of applied study designs. Sample size computation for single group mean, survey type of studies, 2 group studies based on means and proportions or rates, correlation studies and for case-control for assessing the categorical outcome are presented in detail.
format Online
Article
Text
id pubmed-3409926
institution National Center for Biotechnology Information
language English
publishDate 2012
publisher Medknow Publications & Media Pvt Ltd
record_format MEDLINE/PubMed
spelling pubmed-34099262012-08-06 Sample size estimation and power analysis for clinical research studies Suresh, KP Chandrashekara, S J Hum Reprod Sci Review Article Determining the optimal sample size for a study assures an adequate power to detect statistical significance. Hence, it is a critical step in the design of a planned research protocol. Using too many participants in a study is expensive and exposes more number of subjects to procedure. Similarly, if study is underpowered, it will be statistically inconclusive and may make the whole protocol a failure. This paper covers the essentials in calculating power and sample size for a variety of applied study designs. Sample size computation for single group mean, survey type of studies, 2 group studies based on means and proportions or rates, correlation studies and for case-control for assessing the categorical outcome are presented in detail. Medknow Publications & Media Pvt Ltd 2012 /pmc/articles/PMC3409926/ /pubmed/22870008 http://dx.doi.org/10.4103/0974-1208.97779 Text en Copyright: © Journal of Human Reproductive Sciences http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Suresh, KP
Chandrashekara, S
Sample size estimation and power analysis for clinical research studies
title Sample size estimation and power analysis for clinical research studies
title_full Sample size estimation and power analysis for clinical research studies
title_fullStr Sample size estimation and power analysis for clinical research studies
title_full_unstemmed Sample size estimation and power analysis for clinical research studies
title_short Sample size estimation and power analysis for clinical research studies
title_sort sample size estimation and power analysis for clinical research studies
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3409926/
https://www.ncbi.nlm.nih.gov/pubmed/22870008
http://dx.doi.org/10.4103/0974-1208.97779
work_keys_str_mv AT sureshkp samplesizeestimationandpoweranalysisforclinicalresearchstudies
AT chandrashekaras samplesizeestimationandpoweranalysisforclinicalresearchstudies