Med Decis Making - A mathematical approach for evaluating Markov models in continuous time without discrete-event simulation.

Tópicos

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{ cost(1906) reduc(1198) effect(832) }
{ method(1557) propos(1049) approach(1037) }
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Resumo

Markov models are a simple and powerful tool for analyzing the health and economic effects of health care interventions. These models are usually evaluated in discrete time using cohort analysis. The use of discrete time assumes that changes in health states occur only at the end of a cycle period. Discrete-time Markov models only approximate the process of disease progression, as clinical events typically occur in continuous time. The approximation can yield biased cost-effectiveness estimates for Markov models with long cycle periods and if no half-cycle correction is made. The purpose of this article is to present an overview of methods for evaluating Markov models in continuous time. These methods use mathematical results from stochastic process theory and control theory. The methods are illustrated using an applied example on the cost-effectiveness of antiviral therapy for chronic hepatitis B. The main result is a mathematical solution for the expected time spent in each state in a continuous-time Markov model. It is shown how this solution can account for age-dependent transition rates and discounting of costs and health effects, and how the concept of tunnel states can be used to account for transition rates that depend on the time spent in a state. The applied example shows that the continuous-time model yields more accurate results than the discrete-time model but does not require much computation time and is easily implemented. In conclusion, continuous-time Markov models are a feasible alternative to cohort analysis and can offer several theoretical and practical advantages.

Resumo Limpo

markov model simpl power tool analyz health econom effect health care intervent model usual evalu discret time use cohort analysi use discret time assum chang health state occur end cycl period discretetim markov model approxim process diseas progress clinic event typic occur continu time approxim can yield bias costeffect estim markov model long cycl period halfcycl correct made purpos articl present overview method evalu markov model continu time method use mathemat result stochast process theori control theori method illustr use appli exampl costeffect antivir therapi chronic hepat b main result mathemat solut expect time spent state continuoustim markov model shown solut can account agedepend transit rate discount cost health effect concept tunnel state can use account transit rate depend time spent state appli exampl show continuoustim model yield accur result discretetim model requir much comput time easili implement conclus continuoustim markov model feasibl altern cohort analysi can offer sever theoret practic advantag

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