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In this paper we present a review of stochastic trees, a convenient modeling approach for medical treatment decision analyses. Stochastic trees are a generalization of decision trees that incorporate useful features from continuous-time Markov chains. We also discuss StoTree, a freely available software tool for the formulation and solution of stochastic(More)
Suppose you must choose between two pieces of information A and B. In the absence of cost, you would prefer to obtain A rather than B, and in fact would be willing to take more risk to obtain A than B. Nevertheless, you would pay more money for B than for A. Are your preferences consistent with expected utility? The answer is yes; they may very well be. We(More)
In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate the sensitivity of model results to parameter uncertainty. The authors present Bayesian methods for constructing large-sample approximate posterior distributions for probabilities, rates, and relative(More)
I t has come to our attention that inadvertent errors in an earlier article of ours have contributed to a small controversy 1 on the proper procedure for estimating the expected value of perfect information using Monte Carlo simulation. The errors occur in our procedure MC1 2(p101) for forming a Monte Carlo simulation estimate of the information value EVPI(More)
It has not been widely recognized that medical patients as individuals may have goals that are not easily expressed in terms of quality-adjusted life years (QALYs). The QALY model deals with ongoing goals such as reducing pain or maintaining mobility, but goals such as completing an important project or seeing a child graduate from college occur at unique(More)