Refining Estimates of Major Depression Incidence and Episode Duration in Canada Using a Monte Carlo Markov Model
Refining Estimates of Major Depression Incidence and Episode Duration in Canada Using a Monte Carlo Markov Model
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使用蒙特卡洛马尔可夫模型完善对加拿大重度抑郁症发病率和发作持续时间的估计
DOI：
10.1177/0272989x04267008
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发表时间：
2004
影响因子：
3.6
通讯作者：
Robert C. Lee
中科院分区：
文献类型：
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作者：
S. Patten;Robert C. Lee
背景资料。与完整详细的纵向数据相比，对重复性疾病(如重度抑郁症)的连续时期患病率估计更频繁，但很难从这些数据中估计发病率和发作持续时间。发病率和发作持续时间是复发疾病的关键决策建模参数。目标。为了减少在使用连续时期患病率数据的研究得出的重大抑郁发作的全国发病率和发作持续时间估计中本来会出现的偏差，并说明从这种研究估计发病率的方法。方法：研究方法。蒙特卡罗模拟被应用于描述主要抑郁发作的发病和恢复的马尔可夫过程。结果。年发病率为3.1%，发病持续时间为17.1周。与那些没有建模的估计相比，这些估计预计较少受到偏差的影响。结论。这些结果突出了马尔可夫模型在纵向数据分析中的有效性。这里描述的方法可能对决策建模有用，也可以推广到其他慢性病。
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 amethodological 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 andmay be generalizable to other chronic diseases.
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