Latent change score (LCS) models within a continuous-time state-space modeling framework provide a convenient statistical approach for analyzing developmental data. In this study, we evaluate the robustness of such an approach in the context of accelerated longitudinal designs (ALDs). ALDs are especially interesting because they imply a very high rate of planned data missingness. Additionally, most longitudinal studies present unexpected participant attrition leading to unplanned missing data. Therefore, in ALDs, both sources of data missingness are combined. Previous research has shown that ALDs for developmental research allow recovering the population generating process. However, it is unknown how participant attrition impacts the model estimates. We have three goals: (a) to evaluate the robustness of the group-level parameter estimates in scenarios with empirically plausible unplanned data missingness; (b) to evaluate the performance of Kalman scores (KS) imputations for individual data points that were expected but unobserved; and (c) to evaluate the performance of KS imputations for individual data points that were outside the age ranged observed for each case (i.e., to estimate the individual trajectories for the complete age range under study). In general, results showed lack of bias in the simulated conditions. The variability of the estimates increased with lower sample sizes and higher missingness severity. Similarly, we found very accurate estimates of individual scores for both planned and unplanned missing data points. These results are very important for applied practitioners in terms of forecasting and making individual-level decisions. R code is provided to facilitate its implementation by applied researchers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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使用连续时间状态空间模型估计纵向设计中计划和计划外缺失的个体分数。


连续时间状态空间建模框架内的潜在变化评分 (LCS) 模型为分析发育数据提供了一种方便的统计方法。在本研究中，我们评估了这种方法在加速纵向设计（ALD）背景下的稳健性。 ALD 特别有趣，因为它们意味着计划数据丢失率非常高。此外，大多数纵向研究都会出现意外的参与者流失，导致意外的数据缺失。因此，在 ALD 中，数据缺失的两个来源是结合在一起的。先前的研究表明，用于发育研究的 ALD 可以恢复种群生成过程。然而，尚不清楚参与者流失如何影响模型估计。我们有三个目标：（a）在经验上合理的计划外数据缺失的情况下评估组级参数估计的稳健性； (b) 评估预期但未观察到的单个数据点的卡尔曼分数 (KS) 插补的性能； (c) 评估每个案例观察到的年龄范围之外的各个数据点的 KS 插补的性能（即估计所研究的整个年龄范围的各个轨迹）。总的来说，结果表明模拟条件下没有偏差。估计的变异性随着样本量的减小和缺失严重性的增加而增加。同样，我们发现对计划内和计划外缺失数据点的个人得分进行了非常准确的估计。这些结果对于应用从业者的预测和个人决策非常重要。提供 R 代码是为了方便应用研究人员实施。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。