Simulation-based power and sample size calculation for designing interrupted time series analyses of count outcomes in evaluation of health policy interventions
Simulation-based power and sample size calculation for designing interrupted time series analyses of count outcomes in evaluation of health policy interventions
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DOI：
10.1016/j.conctc.2019.100474
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发表时间：
2020-03-01
影响因子：
1.5
通讯作者：
Zhang, Bo
中科院分区：
文献类型：
--
作者：
Liu, Wei;Ye, Shangyuan;Zhang, Bo
目的：本研究的目的是展示中断时间序列 (ITS) 模型的设计、模型和数据分析，该模型用于使用计数结果评估卫生政策、系统或环境干预措施的影响。模拟方法用于对这些研究进行功效和样本量计算。方法：我们以加强多样化入学的转化研究（STRIDE）研究为例，提出了统计结果的 ITS 设计模型和分析。我们使用的模型是观察驱动模型，它将滞后项捆绑在计数结果时间序列的结果的条件均值上。结果：开发了一种基于模拟的方法，使用即用型计算机程序来计算两种类型的 ITS 模型（泊松模型和负二项式模型）的样本量和功效，用于计数结果。进行模拟以估计自相关范围为 -0.9 至 0.9 且具有不同效应大小时分段自回归 (AR) 误差模型的功效。检测相同量级参数的能力差异很大，具体取决于测试水平变化、趋势变化或两者。两个模型之间的功效和样本量以及参数值之间的关系是不同的。结论：本文提供了一个方便的工具，允许研究人员生成样本量，以确保在实施计数结果的 ITS 研究设计时有足够的统计功效。
Objective: The purpose of this study was to present the design, model, and data analysis of an interrupted time series (ITS) model applied to evaluate the impact of health policy, systems, or environmental interventions using count outcomes. Simulation methods were used to conduct power and sample size calculations for these studies.Methods: We proposed the models and analyses of ITS designs for count outcomes using the Strengthening Translational Research in Diverse Enrollment (STRIDE) study as an example. The models we used were observation-driven models, which bundle a lagged term on the conditional mean of the outcome for a time series of count outcomes.Results: A simulation-based approach with ready-to-use computer programs was developed to calculate the sample size and power of two types of ITS models, Poisson and negative binomial, for count outcomes. Simu-lations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorre-lation ranged from -0.9 to 0.9, with various effect sizes. The power to detect the same magnitude of parameters varied largely, depending on the testing level change, the trend change, or both. The relationships between power and sample size and the values of the parameters were different between the two models.Conclusion: This article provides a convenient tool to allow investigators to generate sample sizes that will ensure sufficient statistical power when the ITS study design of count outcomes is implemented.