Division of Epidemiology, University of Utah, Salt Lake City, UT 84108, U.S.A.
Stat Med. 2014 Apr 15;33(8):1395-408. doi: 10.1002/sim.6039. Epub 2013 Nov 20.
Questionnaire-based health status outcomes are often prone to misclassification. When studying the effect of risk factors on such outcomes, ignoring any potential misclassification may lead to biased effect estimates. Analytical challenges posed by these misclassified outcomes are further complicated when simultaneously exploring factors for both the misclassification and health processes in a multi-level setting. To address these challenges, we propose a fully Bayesian mixed hidden Markov model (BMHMM) for handling differential misclassification in categorical outcomes in a multi-level setting. The BMHMM generalizes the traditional hidden Markov model (HMM) by introducing random effects into three sets of HMM parameters for joint estimation of the prevalence, transition, and misclassification probabilities. This formulation not only allows joint estimation of all three sets of parameters but also accounts for cluster-level heterogeneity based on a multi-level model structure. Using this novel approach, both the true health status prevalence and the transition probabilities between the health states during follow-up are modeled as functions of covariates. The observed, possibly misclassified, health states are related to the true, but unobserved, health states and covariates. Results from simulation studies are presented to validate the estimation procedure, to show the computational efficiency due to the Bayesian approach and also to illustrate the gains from the proposed method compared to existing methods that ignore outcome misclassification and cluster-level heterogeneity. We apply the proposed method to examine the risk factors for both asthma transition and misclassification in the Southern California Children's Health Study.
基于问卷的健康状况结果往往容易出现分类错误。在研究危险因素对这些结果的影响时，如果忽略任何潜在的分类错误，可能会导致有偏差的效应估计。当在多层次环境中同时探索分类错误和健康过程的因素时，这些被错误分类的结果所带来的分析挑战变得更加复杂。为了解决这些挑战，我们提出了一种完全贝叶斯混合隐马尔可夫模型（BMHMM），用于处理多水平环境中分类结果的差异分类错误。BMHMM 通过在三个 HMM 参数集中引入随机效应，对流行率、转移和分类错误概率进行联合估计，从而对传统的隐马尔可夫模型（HMM）进行了推广。这种表述不仅允许对所有三组参数进行联合估计，还可以基于多层次模型结构来考虑聚类水平的异质性。使用这种新方法，真实健康状况的流行率和随访期间健康状态之间的转移概率都被建模为协变量的函数。观察到的、可能被错误分类的健康状态与真实的、但未被观察到的健康状态和协变量相关。模拟研究的结果用于验证估计过程，展示由于贝叶斯方法而带来的计算效率，以及说明与忽略结果分类错误和聚类水平异质性的现有方法相比，该方法的优势。我们应用所提出的方法来研究南加州儿童健康研究中哮喘转移和分类错误的风险因素。
