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使用 Scalex 和 ScalaR 计算器进行流行率研究的样本量计算。

Sample size calculation for prevalence studies using Scalex and ScalaR calculators.

机构信息

PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Jalan Tungku Link, Brunei-Muara BE3119, Gadong, Brunei Darussalam.

Faculty of Medicine, Bioscience and Nursing, MAHSA University, Bandar Saujana Putra, Jenjarom, Selangor, Malaysia.

出版信息

BMC Med Res Methodol. 2022 Jul 30;22(1):209. doi: 10.1186/s12874-022-01694-7.

DOI:10.1186/s12874-022-01694-7
PMID:35907796
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9338613/
Abstract

BACKGROUND

Although books and articles guiding the methods of sample size calculation for prevalence studies are available, we aim to guide, assist and report sample size calculation using the present calculators.

RESULTS

We present and discuss four parameters (namely level of confidence, precision, variability of the data, and anticipated loss) required for sample size calculation for prevalence studies. Choosing correct parameters with proper understanding, and reporting issues are mainly discussed. We demonstrate the use of a purposely-designed calculators that assist users to make proper informed-decision and prepare appropriate report.

CONCLUSION

Two calculators can be used with free software (Spreadsheet and RStudio) that benefit researchers with limited resources. It will, hopefully, minimize the errors in parameter selection, calculation, and reporting. The calculators are available at: ( https://sites.google.com/view/sr-ln/ssc ).

摘要

背景

尽管有书籍和文章指导流行率研究的样本量计算方法，但我们旨在使用现有的计算器来指导、协助和报告样本量计算。

结果

我们提出并讨论了流行率研究中样本量计算所需的四个参数（即置信水平、精度、数据变异性和预期损失）。主要讨论了正确理解和选择正确参数以及报告问题。我们展示了使用专门设计的计算器，帮助用户做出正确的知情决策并准备适当的报告。

结论

可以使用免费软件（电子表格和 RStudio）使用两个计算器，这将使资源有限的研究人员受益。它有望最大限度地减少参数选择、计算和报告中的错误。计算器可在以下网址获得：（https://sites.google.com/view/sr-ln/ssc）。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/718c/9338613/b75d4279a937/12874_2022_1694_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/718c/9338613/10ddb730f980/12874_2022_1694_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/718c/9338613/08c095f949bf/12874_2022_1694_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/718c/9338613/96ba08bca65d/12874_2022_1694_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/718c/9338613/b75d4279a937/12874_2022_1694_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/718c/9338613/10ddb730f980/12874_2022_1694_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/718c/9338613/08c095f949bf/12874_2022_1694_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/718c/9338613/96ba08bca65d/12874_2022_1694_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/718c/9338613/b75d4279a937/12874_2022_1694_Fig4_HTML.jpg

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