Medical decision making : an international journal of the Society for Medical Decision Making · 2004
Refining estimates of major depression incidence and episode duration in Canada using a Monte Carlo Markov model.
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Patten Scott B, Lee Robert C
Аннотация
BACKGROUND: Serial period prevalence estimates for recurrent diseases such as major depression are available more frequently than fully detailed longitudinal data, but it is difficult to estimate incidence and episode duration from such data. Incidence and episode duration are critical decision modeling parameters for recurrent diseases.
OBJECTIVES: To reduce bias that would otherwise occur in national incidence and duration-of-episode estimates for major depressive episodes deriving from studies using serial period prevalence data and to illustrate a methodological approach for the estimation of incidence from such studies.
METHODS: Monte Carlo simulation was applied to a Markov process describing incidence and recovery from major depressive episodes.
RESULTS: The annual incidence and episode duration were found to be 3.1% and 17.1 weeks, respectively. These estimates are expected to be less subject to bias than those generated without modeling.
CONCLUSIONS: These results highlight the usefulness of Markov models for analysis of longitudinal data. The methods described here may be useful for decision modeling and may be generalizable to other chronic diseases.
OBJECTIVES: To reduce bias that would otherwise occur in national incidence and duration-of-episode estimates for major depressive episodes deriving from studies using serial period prevalence data and to illustrate a methodological approach for the estimation of incidence from such studies.
METHODS: Monte Carlo simulation was applied to a Markov process describing incidence and recovery from major depressive episodes.
RESULTS: The annual incidence and episode duration were found to be 3.1% and 17.1 weeks, respectively. These estimates are expected to be less subject to bias than those generated without modeling.
CONCLUSIONS: These results highlight the usefulness of Markov models for analysis of longitudinal data. The methods described here may be useful for decision modeling and may be generalizable to other chronic diseases.