The context of comparing two different groups of subjects that are measured repeatedly over time is considered. Our specific focus is on highly variable count data which have a nonnegligible frequency of zeros and have time trends that are difficult to characterize. These challenges are often present when analyzing bacteria or gene expression data sets. Traditional longitudinal data analysis methods, including generalized estimating equations, can be challenged by the features present in these types of data sets. We propose a Bayesian methodology that effectively confronts these challenges. A key feature of the methodology is the use of Gaussian processes to flexibly model the time trends. Inference procedures based on both sharp and interval null hypotheses are discussed, including for the important hypotheses that test for group differences at individual time points. The proposed methodology is illustrated with next-generation sequencing (NGS) data sets corresponding to two different experimental conditions. In particular, the method is applied to a case study containing bacteria counts of mice with chronic and nonchronic wounds to identify potential wound-healing probiotics. The methodology can be applied to similar NGS data sets comparing two groups of subjects.

中文翻译：

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使用高斯过程对计数数据进行贝叶斯纵向趋势分析

考虑比较随时间重复测量的两组不同受试者的背景。我们特别关注高度可变的计数数据，这些数据具有不可忽略的零频率，并且具有难以表征的时间趋势。在分析细菌或基因表达数据集时，通常会出现这些挑战。传统的纵向数据分析方法，包括广义估计方程，可能会受到这些类型数据集中存在的特征的挑战。我们提出了一种有效应对这些挑战的贝叶斯方法。该方法的一个关键特征是使用高斯过程来灵活地模拟时间趋势。讨论了基于尖锐和区间零假设的推理程序，包括在各个时间点检验群体差异的重要假设。所提出的方法用与两种不同实验条件相对应的下一代测序 (NGS) 数据集进行说明。特别是，该方法应用于包含慢性和非慢性伤口小鼠细菌计数的案例研究，以确定潜在的伤口愈合益生菌。该方法可以应用于比较两组受试者的类似 NGS 数据集。