A dynamic Bayesian Markov model for health economic evaluations of interventions in infectious disease

• Published in: BMC medical research methodology Vol. 18; no. 1; p. 82
• Main Authors: Haeussler, Katrin, den Hout, Ardo van, Baio, Gianluca
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 02.08.2018; BioMed Central; BMC
• Subjects: Antibiotics; Bayesian analysis; Bayesian framework; Care and treatment; Communicable diseases; Cost analysis; Cost benefit analysis; Cost-effectiveness analysis; Dynamic Markov model; Economic aspects; Expected values; Health aspects; Health economic evaluation; Herd immunity; Human papillomavirus; Infections; Infectious disease; Infectious diseases; Markov analysis; Medical care, Cost of; Medical research; Medicine, Experimental; Methods; Monte Carlo method; Ordinary differential equations; Pathogens; Pharmacoeconomics; Sensitivity analysis; Vaccines; United Kingdom; United Kingdom > UK; Cost-effectiveness analysis; Herd immunity; Infectious disease; Health economic evaluation; Bayesian framework; Probabilistic sensitivity analysis; Dynamic Markov model
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-018-0541-7

Health economic evaluations of interventions in infectious disease are commonly based on the predictions of ordinary differential equation (ODE) systems or Markov models (MMs). Standard MMs are static, whereas ODE systems are usually dynamic and account for herd immunity which is crucial to prevent overestimation of infection prevalence. Complex ODE systems including distributions on model parameters are computationally intensive. Thus, mainly ODE-based models including fixed parameter values are presented in the literature. These do not account for parameter uncertainty. As a consequence, probabilistic sensitivity analysis (PSA), a crucial component of health economic evaluations, cannot be conducted straightforwardly. We present a dynamic MM under a Bayesian framework. We extend a static MM by incorporating the force of infection into the state allocation algorithm. The corresponding output is based on dynamic changes in prevalence and thus accounts for herd immunity. In contrast to deterministic ODE-based models, PSA can be conducted straightforwardly. We introduce a case study of a fictional sexually transmitted infection and compare our dynamic Bayesian MM to a deterministic and a Bayesian ODE system. The models are calibrated to simulated time series data. By means of the case study, we show that our methodology produces outcome which is comparable to the "gold standard" of the Bayesian ODE system. In contrast to ODE systems in the literature, the dynamic MM includes distributions on all model parameters at manageable computational effort (including calibration). The run time of the Bayesian ODE system is 15 times longer.