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

Research output: Contribution to journalArticle


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.

Original languageEnglish
Pages (from-to)3125-3146
Number of pages22
JournalStatistics in Medicine
Issue number21
Publication statusPublished - Sep 20 2018



  • Bayesian
  • calibration
  • hidden Markov model
  • Markov regression model
  • measurement error

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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