Chiuzan Cody, West Erin A, Duong Jimmy, Cheung Ken Y K, Einstein Andrew J
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.
Division of Cardiology, Departments of Medicine and Radiology, Columbia University Medical Center and New York-Presbyterian Hospital, New York, 10032, USA.
J Nucl Cardiol. 2015 Dec;22(6):1300-13. doi: 10.1007/s12350-015-0256-7. Epub 2015 Sep 24.
Sample size calculation is an important element of research design that investigators need to consider in the planning stage of the study. Funding agencies and research review panels request a power analysis, for example, to determine the minimum number of subjects needed for an experiment to be informative. Calculating the right sample size is crucial to gaining accurate information and ensures that research resources are used efficiently and ethically. The simple question "How many subjects do I need?" does not always have a simple answer. Before calculating the sample size requirements, a researcher must address several aspects, such as purpose of the research (descriptive or comparative), type of samples (one or more groups), and data being collected (continuous or categorical). In this article, we describe some of the most frequent methods for calculating the sample size with examples from nuclear cardiology research, including for t tests, analysis of variance (ANOVA), non-parametric tests, correlation, Chi-squared tests, and survival analysis. For the ease of implementation, several examples are also illustrated via user-friendly free statistical software.
样本量计算是研究设计的一个重要要素,研究人员在研究的规划阶段就需要加以考虑。例如,资助机构和研究评审小组会要求进行效能分析,以确定一项实验能够提供有用信息所需的最少受试者数量。计算合适的样本量对于获取准确信息至关重要,并且能确保研究资源得到有效且符合伦理的使用。简单的问题“我需要多少受试者?”并不总是有简单的答案。在计算样本量要求之前,研究人员必须考虑几个方面,比如研究目的(描述性或比较性)、样本类型(一组或多组)以及所收集的数据(连续性或分类性)。在本文中,我们通过核心脏病学研究中的实例描述一些计算样本量最常用的方法,包括t检验、方差分析(ANOVA)、非参数检验、相关性分析、卡方检验和生存分析。为便于实施,还通过用户友好型免费统计软件举例说明了几个例子。