Probabilistic sensitivity analysis using Monte Carlo simulation. A practical approach.
Probabilistic sensitivity analysis using Monte Carlo simulation. A practical approach.
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DOI：
10.1177/0272989x8500500205
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发表时间：
1985-01-01
期刊：
影响因子：
--
通讯作者：
McNeil, B J
中科院分区：
文献类型：
--
作者：
Doubilet, P;Begg, C B;McNeil, B J
医疗决策分析的数据往往是不可靠的。传统的敏感性分析——根据基线值改变一个或多个概率或效用估计值，以查看最优策略是否发生变化——如果允许两个以上的值同时变化，则会很麻烦。本文介绍了一种实用的概率灵敏度分析方法，该方法同时考虑了所有值的不确定性。假设每个概率和效用的不确定性具有概率分布。为了便于应用，我们使用了一个参数模型，该模型允许每个分布由两个值指定：基线估计和95%置信区间的界限（上限或下限）。在对决策树进行多次模拟后，每个概率和效用在其分布中随机分配一个值，记录如下结果：(a)每种策略的期望效用的均值和标准差；(b)每项策略最优的频率；(c)相对于其他策略，每个策略“购买”或“花费”一定数量效用的频率。正如先前发布的决策分析的应用程序所说明的那样，该技术易于使用，并且可以成为决策分析人员装备的有价值的补充。
The data for medical decision analyses are often unreliable. Traditional sensitivity analysis--varying one or more probability or utility estimates from baseline values to see if the optimal strategy changes--is cumbersome if more than two values are allowed to vary concurrently. This paper describes a practical method for probabilistic sensitivity analysis, in which uncertainties in all values are considered simultaneously. The uncertainty in each probability and utility is assumed to possess a probability distribution. For ease of application we have used a parametric model that permits each distribution to be specified by two values: the baseline estimate and a bound (upper or lower) of the 95 percent confidence interval. Following multiple simulations of the decision tree in which each probability and utility is randomly assigned a value within its distribution, the following results are recorded: (a) the mean and standard deviation of the expected utility of each strategy; (b) the frequency with which each strategy is optimal; (c) the frequency with which each strategy "buys" or "costs" a specified amount of utility relative to the remaining strategies. As illustrated by an application to a previously published decision analysis, this technique is easy to use and can be a valuable addition to the armamentarium of the decision analyst.