Latent Variable Poisson Models for Assessing the Regularity of Circadian Patterns over Time
开发了一种潜变量泊松模型，结合昼夜节律和自回归趋势成分，评估个体计数序列中昼夜节律模式的紊乱程度，并允许协变量影响各成分比例，用青少年体力活动数据验证。
Many researchers in biology and medicine have focused on trying to understand biological rhythms and their potential impact on disease. A common biological rhythm is circadian, where the cycle repeats itself every 24 hours. However, a disturbance of the circadian pattern may be indicative of future disease. In this article, we develop new statistical methodology for assessing the degree of disturbance or irregularity in a circadian pattern for count sequences that are observed over time in a population of individuals. We develop a latent variable Poisson modeling approach with both circadian and stochastic short-term trend (autoregressive latent process) components that allow for individual variation in the degree of each component. A parameterization is proposed for modeling covariate dependence on the proportion of these two model components across individuals. In addition, we incorporate covariate dependence in the overall mean, the magnitude of the trend, and the phase-shift of the circadian pattern. Innovative Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. Several variations of the proposed models are considered and compared using the deviance information criterion. We illustrate this methodology with longitudinal physical activity count data measured in a longitudinal cohort of adolescents.