A currently overlooked application of the latent curve model (LCM) is its use in assessing the consequences of development patterns of change-that is as a predictor of distal outcomes. However, there are additional complications for appropriately specifying and interpreting the distal outcome LCM. Here, we develop a general framework for understanding the sensitivity of the distal outcome LCM to the choice of time coding, focusing on the regressions of the distal outcome on the latent growth factors. Using artificial and real-data examples, we highlight the unexpected changes in the regression of the slope factor which stand in contrast to prior work on time coding effects, and develop a framework for estimating the distal outcome LCM at a point in the trajectory-known as the aperture-which maximizes the interpretability of the effects. We also outline a prioritization approach developed for assessing incremental validity to obtain consistently interpretable estimates of the effect of the slope. Throughout, we emphasize practical steps for understanding these changing predictive effects, including graphical approaches for assessing regions of significance similar to those used to probe interaction effects. We conclude by providing recommendations for applied research using these models and outline an agenda for future work in this area. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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潜在生长因子作为远端结果的预测因子。


潜在曲线模型 (LCM) 目​​前被忽视的一个应用是其用于评估变化发展模式的后果，即作为远端结果的预测因子。然而，适当指定和解释远端结果 LCM 还存在其他并发症。在这里，我们开发了一个通用框架来理解远端结果 LCM 对时间编码选择的敏感性，重点关注远端结果对潜在生长因子的回归。使用人工和真实数据示例，我们强调了斜率因子回归中的意外变化，这与之前关于时间编码效应的工作形成鲜明对比，并开发了一个框架，用于估计轨迹已知点上的远端结果 LCM作为光圈——最大化效果的可解释性。我们还概述了一种用于评估增量有效性的优先顺序方法，以获得对斜率影响的一致可解释的估计。在整个过程中，我们强调理解这些不断变化的预测效应的实际步骤，包括评估与探测相互作用效应类似的重要区域的图形方法。最后，我们为使用这些模型的应用研究提供建议，并概述了该领域未来工作的议程。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。