In a longitudinal clinical registry, different measurement instruments might have been used for assessing individuals at different time points. To combine them, we investigate deep learning techniques for obtaining a joint latent representation, to which the items of different measurement instruments are mapped. This corresponds to domain adaptation, an established concept in computer science for image data. Using the proposed approach as an example, we evaluate the potential of domain adaptation in a longitudinal cohort setting with a rather small number of time points, motivated by an application with different motor function measurement instruments in a registry of spinal muscular atrophy (SMA) patients. There, we model trajectories in the latent representation by ordinary differential equations (ODEs), where person-specific ODE parameters are inferred from baseline characteristics. The goodness of fit and complexity of the ODE solutions then allows to judge the measurement instrument mappings. We subsequently explore how alignment can be improved by incorporating corresponding penalty terms into model fitting. To systematically investigate the effect of differences between measurement instruments, we consider several scenarios based on modified SMA data, including scenarios where a mapping should be feasible in principle and scenarios where no perfect mapping is available. While misalignment increases in more complex scenarios, some structure is still recovered, even if the availability of measurement instruments depends on patient state. A reasonable mapping is feasible also in the more complex real SMA dataset. These results indicate that domain adaptation might be more generally useful in statistical modeling for longitudinal registry data.

翻译：在纵向临床注册研究中，不同测量工具可能被用于不同时间点的个体评估。为整合这些数据，我们研究了深度学习技术以获取联合潜在表征，将不同测量工具的项目映射至该表征。这对应了计算机科学中图像数据领域的成熟概念——领域适应。以所提方法为例，我们评估了领域适应在时间点较少的纵向队列设置中的潜力，该研究受到脊髓性肌萎缩症（SMA）患者注册研究中不同运动功能测量工具应用的启发。在此类应用中，我们通过常微分方程（ODE）对潜在表征中的轨迹进行建模，其中个体特定ODE参数基于基线特征推断得出。ODE解的拟合优度与复杂度随后可用于判断测量工具映射的质量。我们进一步探索如何通过将相应惩罚项纳入模型拟合来改进对齐效果。为系统研究测量工具间差异的影响，我们基于修改后的SMA数据考虑了多种场景，包括理论上映射可行的场景以及无法实现完美映射的场景。尽管在复杂场景中对齐偏差有所增大，但即使在测量工具可用性取决于患者状态的情况下，仍能恢复部分结构。在更复杂的真实SMA数据集中，合理的映射也是可行的。这些结果表明，领域适应可能更广泛地适用于纵向注册数据的统计建模。