Distributional representations of data collected using digital health technologies have been shown to outperform scalar summaries for clinical prediction, with carefully quantified tail-behavior often driving the gains. Motivated by these findings, we propose a unified generalized odds (GO) framework that represents subject-specific distributions through ratios of probabilities over arbitrary regions of the sample space, subsuming hazard, survival, and residual life representations as special cases. We develop a scale-on-odds regression model using spline-based functional representations with penalization for efficient estimation. Applied to wrist-worn accelerometry data from the HEAL-MS study, generalized odds models yield improved prediction of Expanded Disability Status Scale (EDSS) scores compared to classical scalar and survival-based approaches, demonstrating the value of odds-based distributional covariates for modeling DHT data.

翻译：数字健康技术采集的数据采用分布表示已被证明在临床预测中优于标量摘要，其中经过精细量化的尾部行为通常是提升预测性能的关键。受这些发现启发，我们提出了一个统一的广义比率框架，该框架通过样本空间任意区域上的概率比值来表示个体特异性分布，将风险函数、生存函数及剩余寿命表示等纳入其特例范畴。我们开发了一种基于样条函数表示并结合惩罚项的比率-标量回归模型以实现高效估计。将该模型应用于HEAL-MS研究中的腕戴式加速度计数据，与经典的标量方法和基于生存分析的方法相比，广义比率模型在扩展残疾状态量表评分预测方面表现出更优性能，这证实了基于比率的分布协变量在建模数字健康技术数据中的价值。