Bayesian measurement-error-driven hidden Markov regression model for calibrating the effect of covariates on multistate outcomes: Application to androgenetic alopecia

Amy Ming Fang Yen, Hsiu Hsi Chen

研究成果: 雜誌貢獻文章同行評審

2 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
頁(從 - 到)3125-3146
頁數22
期刊Statistics in Medicine
37
發行號21
DOIs
出版狀態已發佈 - 九月 20 2018

ASJC Scopus subject areas

  • 流行病學
  • 統計與概率

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