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Addressing confounding when estimating the effects of latent classes on a distal outcome

机译：在评估潜在类别对远端结局的影响时解决混淆

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Confounding is widely recognized in settings where all variables are fully observed, yet recognition of and statistical methods to address confounding in the context of latent class regression are slowly emerging. In this study we focus on confounding when regressing a distal outcome on latent class; extending standard confounding methods is not straightforward when the treatment of interest is a latent variable. We describe a recent 1-step method, as well as two 3-step methods (modal and pseudoclass assignment) that incorporate propensity score weighting. Using simulated data, we compare the performance of these three adjusted methods to an unadjusted 1-step and unadjusted 3-step method. We also present an applied example regarding adolescent substance use treatment that examines the effect of treatment service class on subsequent substance use problems. Our simulations indicated that the adjusted 1-step method and both adjusted 3-step methods significantly reduced bias arising from confounding relative to the unadjusted 1-step and 3-step approaches. However, the adjusted 1-step method performed better than the adjusted 3-step methods with regard to bias and 95 % CI coverage, particularly when class separation was poor. Our applied example also highlighted the importance of addressing confounding-both unadjusted methods indicated significant differences across treatment classes with respect to the outcome, yet these class differences were not significant when using any of the three adjusted methods. Potential confounding should be carefully considered when conducting latent class regression with a distal outcome; failure to do so may results in significantly biased effect estimates or incorrect inferences.
机译：在充分观察到所有变量的环境中，混淆是公认的，但是在潜在类别回归的背景下，解决混淆的认识和统计方法正在慢慢出现。在这项研究中，我们集中在使潜伏类的远端结局退步时感到困惑。当感兴趣的治疗是一个潜在变量时，扩展标准混杂方法并不是简单明了的。我们描述了一种最近的1步方法，以及结合了倾向得分加权的2个3步方法（模态和伪类分配）。使用模拟数据，我们将这三种调整后的方法的性能与未经调整的1步和未经调整的3步方法进行了比较。我们还提供了一个有关青少年物质使用治疗的应用示例，该示例研究了治疗服务类别对后续物质使用问题的影响。我们的仿真表明，相对于未经调整的1步和3步方法，调整的1步方法和调整的3步方法均显着降低了因混杂而产生的偏差。但是，就偏倚和95％CI覆盖率而言，调整的1步方法比调整的3步方法效果更好，尤其是在类别分离差的情况下。我们的应用实例还强调了解决混杂问题的重要性，这两种未经调整的方法均表明治疗类别之间在结果方面存在显着差异，但使用三种调整方法中的任何一种时，这些类别差异均不显着。当进行潜在的远端回归时，应仔细考虑潜在的混淆。否则可能会导致效果估计有明显偏差或推论不正确。

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