Stevenson Mark A
Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC, Australia.
Front Vet Sci. 2021 Feb 17;7:539573. doi: 10.3389/fvets.2020.539573. eCollection 2020.
In the design of intervention and observational epidemiological studies sample size calculations are used to provide estimates of the minimum number of observations that need to be made to ensure that the stated objectives of a study are met. Justification of the number of subjects enrolled into a study and details of the assumptions and methodologies used to derive sample size estimates are now a mandatory component of grant application processes by funding agencies. Studies with insufficient numbers of study subjects run the risk of failing to identify differences among treatment or exposure groups when differences do, in fact, exist. Selection of a number of study subjects greater than that actually required results in a wastage of time and resources. In contrast to human epidemiological research, individual study subjects in a veterinary setting are almost always aggregated into hierarchical groups and, for this reason, sample size estimates calculated using formulae that assume data independence are not appropriate. This paper provides an overview of the reasons researchers might need to calculate an appropriate sample size in veterinary epidemiology and a summary of sample size calculation methods. Two approaches are presented for dealing with lack of data independence when calculating sample sizes: (1) inflation of crude sample size estimates using a design effect; and (2) simulation-based methods. The advantage of simulation methods is that appropriate sample sizes can be estimated for complex study designs for which formula-based methods are not available. A description of the methodological approach for simulation is described and a worked example provided.
在干预性和观察性流行病学研究的设计中,样本量计算用于估计所需的最少观察次数,以确保研究的既定目标得以实现。说明纳入研究的受试者数量以及用于得出样本量估计值的假设和方法的详细信息,现在是资助机构拨款申请流程的一个强制性组成部分。研究对象数量不足的研究存在风险,即当治疗组或暴露组之间确实存在差异时,却无法识别这些差异。选择超过实际所需数量的研究对象会导致时间和资源的浪费。与人类流行病学研究不同,兽医领域的个体研究对象几乎总是被聚集到分层组中,因此,使用假设数据独立的公式计算样本量估计值是不合适的。本文概述了研究人员在兽医流行病学中可能需要计算合适样本量的原因,并总结了样本量计算方法。在计算样本量时,提出了两种处理数据缺乏独立性的方法:(1)使用设计效应来增大粗略样本量估计值;(2)基于模拟的方法。模拟方法的优点是,可以为复杂的研究设计估计合适的样本量,而基于公式的方法无法用于此类设计。本文描述了模拟的方法学方法,并提供了一个实例。