Latent growth models (LGMs) with a distal outcome allow researchers to assess longer-term patterns, and to detect the need to start a (preventive) treatment or intervention in an early stage. The aim of the current simulation study is to examine the performance of an LGM with a continuous distal outcome under maximum likelihood (ML) and Bayesian estimation with default and informative priors, under varying sample sizes, effect sizes and slope variance values. We conclude that caution is needed when predicting a distal outcome from an LGM when the: (1) sample size is small; and (2) amount of variation around the latent slope is small, even with a large sample size. We recommend against the use of ML and Bayesian estimation with Mplus default priors in these situations to avoid severely biased estimates. Recommendations for substantive researchers working with LGMs with distal outcomes are provided based on the simulation results.

中文翻译：

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从潜在增长模型预测远端结果变量：ML 与贝叶斯估计

具有远端结果的潜在生长模型 (LGM) 使研究人员能够评估长期模式，并检测是否需要在早期开始（预防性）治疗或干预。当前模拟研究的目的是检查在最大似然 (ML) 和贝叶斯估计下具有连续远端结果的 LGM 的性能，在默认和信息先验的情况下，在不同的样本量、效应量和斜率方差值下。我们得出的结论是，在以下情况下预测 LGM 的远期结果时需要谨慎：(1) 样本量小；(2) 即使样本量很大，潜在斜率周围的变化量也很小。我们建议不要在这些情况下使用带有 Mplus 默认先验的 ML 和贝叶斯估计，以避免严重偏差的估计。