Latent growth curve mediation models are increasingly used to assess mechanisms of behavior change. For latent growth mediation model, like any another mediation model, even with random treatment assignment, a critical but untestable assumption for valid and unbiased estimates of the indirect effects is that there should be no omitted variable that confounds indirect effects. One way to address this untestable assumption is to conduct sensitivity analysis to assess whether the inference about an indirect effect would change under varying degrees of confounding bias. We developed a sensitivity analysis technique for a latent growth curve mediation model. We compute the biasing effect of confounding on point and confidence interval estimates of the indirect effects in a structural equation modeling framework. We illustrate sensitivity plots to visualize the effects of confounding on each indirect effect and present an empirical example to illustrate the application of the sensitivity analysis.

中文翻译：

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潜在增长曲线中介模型中不遗漏混杂因素假设的敏感性分析


潜在增长曲线中介模型越来越多地用于评估行为改变的机制。对于潜在增长中介模型，与任何其他中介模型一样，即使采用随机治疗分配，对于间接效应的有效和无偏估计的一个关键但不可检验的假设是，不应遗漏任何混淆间接效应的变量。解决这种无法检验的假设的一种方法是进行敏感性分析，以评估关于间接效应的推论是否会在不同程度的混杂偏差下发生变化。我们开发了一种用于潜在增长曲线中介模型的敏感性分析技术。我们计算结构方程建模框架中间接效应的点和置信区间估计的混杂效应的偏差效应。我们展示了敏感性图，以可视化混杂因素对每个间接效应的影响，并提供了一个实证示例来说明敏感性分析的应用。