Modeling comorbidity of chronic diseases using coupled hidden Markov model with bivariate discrete copula

Creative Commons License

Oflaz Z., Yozgatligil C., Kestel A. S.

STATISTICAL METHODS IN MEDICAL RESEARCH, vol.32, no.4, pp.829-849, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 4
  • Publication Date: 2023
  • Doi Number: 10.1177/09622802231155100
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ASSIA, CAB Abstracts, CINAHL, EMBASE, MEDLINE, zbMATH
  • Page Numbers: pp.829-849
  • Keywords: Coupled hidden Markov model, discrete copula, dependency in hidden process, comorbidity, chronic diseases, PULMONARY-HYPERTENSION, COMPETING RISKS, INCOMPLETE DATA, CLASSIFICATION, ASSOCIATION, INFERENCE, NETWORKS, HEALTH, CHAIN
  • Middle East Technical University Affiliated: Yes


A range of chronic diseases have a significant influence on each other and share common risk factors. Comorbidity, which shows the existence of two or more diseases interacting or triggering each other, is an important measure for actuarial valuations. The main proposal of the study is to model parallel interacting processes describing two or more chronic diseases by a combination of hidden Markov theory and copula function. This study introduces a coupled hidden Markov model with the bivariate discrete copula function in the hidden process. To estimate the parameters of the model and deal with the numerical intractability of the log-likelihood, we use a variational expectation maximization algorithm. To perform the variational expectation maximization algorithm, a lower bound of the model's log-likelihood is defined, and estimators of the parameters are computed in the M-part. A possible numerical underflow occurring in the computation of forward-backward probabilities is solved. The simulation study is conducted for two different levels of association to assess the performance of the proposed model, resulting in satisfactory findings. The proposed model was applied to hospital appointment data from a private hospital. The model defines the dependency structure of unobserved disease data and its dynamics. The application results demonstrate that the model is useful for investigating disease comorbidity when only population dynamics over time and no clinical data are available.