Stratification is a very commonly used approach in biomedical studies to handle sample heterogeneity arising from, for examples, clinical units, patient subgroups, or missing-data. A key rationale behind such approach is to overcome potential sampling biases in statistical inference. Two issues of such stratification-based strategy are (i) whether individual strata are sufficiently distinctive to warrant stratification, and (ii) sample size attrition resulted from the stratification may potentially lead to loss of statistical power. To address these issues, we propose a penalized generalized estimating equations approach to reducing the complexity of parametric model structures due to excessive stratification. Specifically, we develop a data-driven fusion learning approach for longitudinal data that improves estimation efficiency by integrating information across similar strata, yet still allows necessary separation for stratum-specific conclusions. The proposed method is evaluated by simulation studies and applied to a motivating example of psychiatric study to demonstrate its usefulness in real world settings.

中文翻译：

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纵向数据分析中的后分层融合学习

分层是生物医学研究中一种非常常用的方法，用于处理由例如临床单位、患者亚组或缺失数据引起的样本异质性。这种方法背后的一个关键原理是克服统计推断中潜在的抽样偏差。这种基于分层的策略的两个问题是 (i) 单个分层是否足够独特以保证分层，以及 (ii) 分层导致的样本量损耗可能会导致统计功效的丧失。为了解决这些问题，我们提出了一种惩罚广义估计方程方法，以减少由于过度分层而导致的参数模型结构的复杂性。具体来说，我们为纵向数据开发了一种数据驱动的融合学习方法，该方法通过整合相似层之间的信息来提高估计效率，但仍然允许对特定层的结论进行必要的分离。所提出的方法通过模拟研究进行评估，并应用于精神病学研究的一个激励示例，以证明其在现实世界环境中的有用性。