School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Stat Med. 2018 Sep 20;37(21):3125-3146. doi: 10.1002/sim.7813. Epub 2018 May 21.
Multistate Markov regression models used for quantifying the effect size of state-specific covariates pertaining to the dynamics of multistate outcomes have gained popularity. However, the measurements of multistate outcome are prone to the errors of classification, particularly when a population-based survey/research is involved with proxy measurements of outcome due to cost consideration. Such a misclassification may affect the effect size of relevant covariates such as odds ratio used in the field of epidemiology. We proposed a Bayesian measurement-error-driven hidden Markov regression model for calibrating these biased estimates with and without a 2-stage validation design. A simulation algorithm was developed to assess various scenarios of underestimation and overestimation given nondifferential misclassification (independent of covariates) and differential misclassification (dependent on covariates). We applied our proposed method to the community-based survey of androgenetic alopecia and found that the effect size of the majority of covariate was inflated after calibration regardless of which type of misclassification. Our proposed Bayesian measurement-error-driven hidden Markov regression model is practicable and effective in calibrating the effects of covariates on multistate outcome, but the prior distribution on measurement errors accrued from 2-stage validation design is strongly recommended.
多状态马尔可夫回归模型常用于量化与多状态结果动态相关的特定状态协变量的效应大小，目前已得到广泛应用。然而，多状态结局的测量容易受到分类错误的影响，尤其是在涉及基于人群的调查/研究，由于成本考虑而使用结局的替代测量时。这种错误分类可能会影响相关协变量（如流行病学领域中使用的优势比）的效应大小。我们提出了一种贝叶斯测量误差驱动的隐马尔可夫回归模型，用于在有和没有两阶段验证设计的情况下校准这些有偏估计。开发了一种模拟算法，以评估在非差异分类（与协变量无关）和差异分类（与协变量有关）的情况下，各种低估和高估情况。我们将所提出的方法应用于社区雄激素性脱发调查，发现无论哪种类型的分类错误，在经过校准后，大多数协变量的效应大小都增加了。我们提出的贝叶斯测量误差驱动的隐马尔可夫回归模型在校准协变量对多状态结局的影响方面是可行且有效的，但强烈建议使用两阶段验证设计获得的测量误差的先验分布。
