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

ABSTRACT: 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 nond

SUBMITTER: Yen AM 

PROVIDER: S-EPMC6120552 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature