标题： 跳过跟踪：一种贝叶斯分层模型，用于大型移动健康队列中自我跟踪的月经周期长度和规律性 显示英文标题

标题： SkipTrack: A Bayesian Hierarchical Model for Self-tracked Menstrual Cycle Length and Regularity in Large Mobile Health Cohorts

摘要： 月经周期长度和规律性是具有多种急性和慢性健康状况意义的重要生命体征。 从周期跟踪移动健康应用程序中获得的大数据集被用于研究各种协变量对月经周期长度和规律性的影响。 这些分析的一个局限性是，如果用户在应用程序中跳过任何与周期相关的出血日记录，记录的周期长度可能会被错误地放大。 在这里，我们提出了SkipTrack，一种新颖的贝叶斯分层框架，在考虑周期跟踪可能跳过的不确定性的同时，检验月经周期长度和规律性的基线和时变效应。 在模拟中，我们通过展示事先指定周期跳过的竞争方法比SkipTrack模型更容易受到估计偏差和过度自信的问题的影响，证明了SkipTrack模型的优势。 最后，我们将SkipTrack框架应用于Apple Women's Healthy Study的数据，这是一个基于美国的数字队列（在研究入组时提供同意），以检查年龄、BMI和种族/民族与月经周期长度和规律性之间的关联模式。 显示英文摘要

摘要： Menstrual cycle length and regularity are important vital signs with implications for a variety of acute and chronic health conditions. Large datasets derived from cycle-tracking mobile health apps are being used to investigate the effects of various covariates on menstrual cycle length and regularity. One limitation on these analyses is that recorded cycle lengths can be incorrectly inflated if users skip tracking any cycle related bleeding days in the app. Here we present SkipTrack, a novel Bayesian hierarchical framework for examining baseline and time-varying effects on menstrual cycle length and regularity while accounting for the uncertainty of possible skips in cycle tracking. In simulations we demonstrate the superiority of the SkipTrack model by showing that competing methods which specify cycle skips a priori are more susceptible to issues of estimation bias and overconfidence than the SkipTrack model. Finally, we apply the SkipTrack framework to data from the Apple Women's Healthy Study, a US-based digital cohort (consent provided at study enrollment) to examine patterns of association between age, BMI and race/ethnicity, and menstrual cycle length and regularity.