ABSTRACT

We propose a Bayesian latent variable model to allow estimation of the covariate-adjusted relationships between an outcome and a small number of latent exposure variables, using data from multiple observed exposures. Each latent variable is assumed to be represented by multiple exposures, where membership of the observed exposures to latent groups is unknown. Our model assumes that one measured exposure variable can be considered as a sentinel marker for each latent variable, while membership of the other measured exposures is estimated using MCMC sampling based on a classical measurement error model framework. We illustrate our model using data on multiple cytokines and birth weight from the Seychelles Child Development Study, and evaluate the performance of our model in a simulation study. Classification of cytokines into Th1 and Th2 cytokine classes in the Seychelles study revealed some differences from standard Th1/Th2 classifications. In simulations, our model correctly classified measured exposures into latent groups, and estimated model parameters with little bias and with coverage that was similar to the oracle model.

 抽象的

我们提出了一个贝叶斯潜在变量模型，允许使用来自多个观察到的暴露的数据来估计结果与少量潜在暴露变量之间的协变量调整关系。假设每个潜在变量由多次暴露表示，其中观察到的潜在组暴露的成员资格是未知的。我们的模型假设一个测量的暴露变量可以被视为每个潜在变量的哨兵标记，而其他测量的暴露变量的成员资格是使用基于经典测量误差模型框架的 MCMC 采样来估计的。我们使用塞舌尔儿童发育研究中的多种细胞因子和出生体重的数据来说明我们的模型，并评估我们的模型在模拟研究中的表现。塞舌尔研究中将细胞因子分为 Th1 和 Th2 细胞因子类别，揭示了与标准 Th1/Th2 分类的一些差异。在模拟中，我们的模型将测量的暴露正确分类为潜在组，并以很小的偏差估计模型参数，并且覆盖范围与预言机模型类似。