Sharples L D, Taylor G I, Faddy M
MRC Biostatistics Unit, Cambridge, UK.
J Epidemiol Biostat. 2001;6(4):349-55. doi: 10.1080/13595220152601828.
Markov and semi-Markov models are increasingly used in clinical and public health epidemiology to represent disease processes. We present a Markov model of events following lung transplantation as a case study in clinical epidemiology.
A five-state discrete-time Markov model with two-way transitions between acute event states is applied to the analysis of 356 lung transplant patients. A two-state continuous time Markov model for chronic disease onset is fitted. Values of transition parameters are estimated by maximum likelihood using numerical methods.
Accurate estimates of acute and chonic event rates, and survival probabilities are calculated from transition probabilities. Costs attributed to different acute and chronic states are calculated.
Transition models provide a useful and flexible representation of acute and chronic events and can be used to explore the economic impact of changes in therapy.
马尔可夫模型和半马尔可夫模型在临床和公共卫生流行病学中越来越多地用于描述疾病进程。我们提出一个肺移植后事件的马尔可夫模型，作为临床流行病学的一个案例研究。
一个具有急性事件状态间双向转变的五状态离散时间马尔可夫模型应用于356例肺移植患者的分析。拟合了一个用于慢性病发病的两状态连续时间马尔可夫模型。使用数值方法通过最大似然估计转变参数的值。
根据转变概率计算急性和慢性事件发生率以及生存概率的准确估计值。计算归因于不同急性和慢性状态的成本。
转变模型为急性和慢性事件提供了一种有用且灵活的描述，可用于探讨治疗变化的经济影响。
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