Latent class analysis is an intuitive tool to characterize disease phenotype heterogeneity. With data more frequently collected on multiple phenotypes in chronic disease studies, it is of rising interest to investigate how the latent classes embedded in one phenotype are related to another phenotype. Motivated by a cohort with mild cognitive impairment (MCI) from the Uniform Data Set (UDS), we propose and study a time-dependent structural model to evaluate the association between latent classes and competing risk outcomes that are subject to missing failure types. We develop a two-step estimation procedure which circumvents latent class membership assignment and is rigorously justified in terms of accounting for the uncertainty in classifying latent classes. The new method also properly addresses the realistic complications for competing risks outcomes, including random censoring and missing failure types. The asymptotic properties of the resulting estimator are established. Given that the standard bootstrapping inference is not feasible in the current problem setting, we develop analytical inference procedures, which are easy to implement. Our simulation studies demonstrate the advantages of the proposed method over benchmark approaches. We present an application to the MCI data from UDS, which uncovers a detailed picture of the neuropathological relevance of the baseline MCI subgroups.

中文翻译：

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使用多表型数据评估潜在类别与竞争风险结果之间的关联


潜在类别分析是表征疾病表型异质性的直观工具。随着慢性病研究中更频繁地收集多种表型的数据，研究一种表型中嵌入的潜在类别如何与另一种表型相关越来越引起人们的兴趣。受统一数据集（UDS）中轻度认知障碍（MCI）队列的启发，我们提出并研究了一种时间依赖性结构模型，以评估潜在类别与受缺失失败类型影响的竞争风险结果之间的关联。我们开发了一个两步估计程序，它规避了潜在类别成员资格分配，并且在考虑潜在类别分类的不确定性方面经过严格论证。新方法还正确解决了竞争风险结果的现实复杂性，包括随机审查和缺失的故障类型。建立了所得估计量的渐近性质。鉴于标准引导推理在当前问题设置中不可行，我们开发了易于实现的分析推理程序。我们的模拟研究证明了所提出的方法相对于基准方法的优势。我们提出了对 UDS 的 MCI 数据的应用，它揭示了基线 MCI 亚组的神经病理学相关性的详细图片。