Baghfalaki Taban
a Department of Statistics, Faculty of Mathematical Sciences , Tarbiat Modares University , Tehran , Iran.
J Biopharm Stat. 2019;29(2):244-270. doi: 10.1080/10543406.2018.1535501. Epub 2018 Oct 25.
Longitudinal study designs are commonly applied in much scientific research, especially in the medical, social, and economic sciences. Longitudinal studies allow researchers to measure changes in each individual's responses over time and often have higher statistical power than cross-sectional studies. Choosing an appropriate sample size is a crucial step in a successful study. In longitudinal studies, because of the complexity of their design, including the selection of the number of individuals and the number of repeated measurements, sample size determination is less studied. This paper uses a simulation-based method to determine the sample size from a Bayesian perspective. For this purpose, several Bayesian criteria for sample size determination are used, of which the most important one is the Bayesian power criterion. We determine the sample size of a longitudinal study based on the scientific question of interest, by the choice of an appropriate model. Most of the methods of determining sample size are based on the definition of a single hypothesis. In this paper, in addition to using this method, we determine the sample size using multiple hypotheses. Using several examples, the proposed Bayesian methods are illustrated and discussed.
纵向研究设计在许多科学研究中普遍应用，尤其是在医学、社会科学和经济科学领域。纵向研究使研究人员能够测量每个个体的反应随时间的变化，并且通常比横断面研究具有更高的统计效力。选择合适的样本量是成功开展一项研究的关键步骤。在纵向研究中，由于其设计的复杂性，包括个体数量的选择和重复测量的次数，样本量的确定较少被研究。本文从贝叶斯视角使用基于模拟的方法来确定样本量。为此，使用了几种用于确定样本量的贝叶斯标准，其中最重要的是贝叶斯效力标准。我们根据感兴趣的科学问题，通过选择合适的模型来确定纵向研究的样本量。大多数确定样本量的方法基于单一假设的定义。在本文中，除了使用这种方法外，我们还使用多个假设来确定样本量。通过几个例子对所提出的贝叶斯方法进行了说明和讨论。
