The benefits of using semi-continuous and continuous models to analyze binge eating data: A Monte Carlo investigation.
The benefits of using semi-continuous and continuous models to analyze binge eating data: A Monte Carlo investigation.
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DOI：
10.1002/eat.22351
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发表时间：
2015-09
期刊：
影响因子：
--
通讯作者：
Yu J
中科院分区：
文献类型：
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作者：
Grotzinger A;Hildebrandt T;Yu J
暴饮暴食的改变通常是针对饮食病理个体的干预措施的主要结果。存在一系列统计模型来处理这些类型的频率分布，但很少有经验证据来指导统计模型的适当选择。蒙特卡罗模拟被用来调查半连续模型相对于连续模型在各种情况下相关的暴饮暴食治疗研究的效用。半连续模型对总体的估计更准确，而连续模型在存在较高水平的缺失数据时具有更高的把握度。目前的研究结果通常支持使用半连续模型应用于暴食数据，总样本量约为200，足以检测中度治疗效果。然而，具有大量缺失数据的模型对于连续模型产生了更有利的功效估计。
Change in binge eating is typically a primary outcome for interventions targeting individuals with eating pathology. A range of statistical models exist to handle these types of frequency distributions, but little empirical evidence exists to guide the appropriate choice of statistical model. Monte Carlo simulations were used to investigate the utility of semi-continuous models relative to continuous models in various situations relevant to binge eating treatment studies. Semi-continuous models yielded more accurate estimates of the population, while continuous models were higher powered when higher levels of missing data were present. The present findings generally support the use of semi-continuous models applied to binge eating data, with total sample sizes of roughly 200 being adequately powered to detect moderate treatment effects. However, models with a significant amount of missing data yielded more favorable power estimates for continuous models.