Sample Size Calculation in Medical Research: A Primer

J. Charan, Rimplejeet Kaur, P. Bhardwaj, Kuldeep Singh, S. Ambwani, S. Misra
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引用次数: 7

Abstract

Abstract Quality of research is determined by many factors and one such climacteric factor is sample size. Inability to use correct sample size in study might lead to fallacious results in the form of rejection of true findings or approval of false results. Too large sample size is wastage of resources and use of too small sample size might fail to answer the research question or provide imprecise results and may question the validity of study. Despite being such a paramount aspect of research, the knowledge about sample size calculation is sparse among researchers. Why is it important to calculate sample size; when to calculate it; how to calculate it and what details about sample size calculation should be reported in research protocols or articles; are the lesser known basics to majority of researchers. The present review is directed to address these aforementioned fundamentals about sample size. Sample size should be calculated during the initial phase of planning of study. Several components are required for sample size calculation such as effect size, type-1 error, type-2 error, and variance. Researchers must be aware that there are different formulas for calculating sample size for different types of study designs. The researcher must include details about sample size calculation in the methodology section, so that it can be justified and it also adds to the transparency of the study. The literature about calculation of sample size for different study designs is scattered over many textbooks and journals. Scrupulous literature search was conducted to find the passable information for this review. This paper presents the sample size calculation formulas in a single review in a simplified manner with relevant examples, so that researchers may adequately use them in their research.
医学研究中的样本量计算:入门
研究的质量由许多因素决定,其中一个关键因素是样本量。在研究中不能使用正确的样本量可能会导致错误的结果,表现为拒绝真实的发现或认可虚假的结果。过大的样本量是对资源的浪费,使用过小的样本量可能无法回答研究问题或提供不精确的结果,并可能质疑研究的有效性。尽管这是研究的一个重要方面,但研究人员对样本量计算的了解却很少。为什么计算样本量很重要;何时计算;如何计算,以及在研究方案或文章中应报告样本量计算的哪些细节;是大多数研究人员鲜为人知的基础知识。本综述旨在解决上述关于样本量的基本问题。样本量应在研究规划的初始阶段进行计算。样本量计算需要几个分量,如效应大小、类型1误差、类型2误差和方差。研究人员必须意识到,对于不同类型的研究设计,有不同的计算样本量的公式。研究人员必须在方法论部分包括样本量计算的细节,这样才能证明其合理性,并增加研究的透明度。关于不同研究设计的样本量计算的文献分散在许多教科书和期刊上。进行了仔细的文献检索,以找到本综述的可通过信息。本文以简化的方式在一篇综述中介绍了样本量的计算公式,并附上了相关的例子,以便研究人员在研究中充分使用它们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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19
审稿时长
12 weeks
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