promoting access to White Rose
نویسنده
چکیده
23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 Partial EVPI calculations can quantify the value of learning about particular subsets of uncertain parameters in decision models. Published case studies have used different computational approaches. This paper examines the computation of partial EVPI estimates via Monte-Carlo sampling algorithms. Our mathematical definition shows two nested expectations, which must be evaluated separately because of the need to compute a maximum between them. A generalised Monte-Carlo sampling algorithm uses nested simulation with an outer loop to sample parameters of interest and, conditional upon these, an inner loop to sample remaining uncertain parameters. Alternative computation methods and ‘shortcut’ algorithms are discussed and mathematical conditions for their use are considered. Maxima of MonteCarlo estimates of expectations are biased upwards, and we demonstrate that using small samples results in biased EVPI estimates. Three case studies illustrate (i) the bias due to maximisation, and also the inaccuracy of shortcut algorithms (ii) when correlated variables are present and (iii) when there is nonlinearity in net-benefit functions. If relatively small correlation or non-linearity is present, then the ‘shortcut’ algorithm can be substantially inaccurate. Empirical investigation of the numbers of MonteCarlo samples suggest that fewer samples on the outer level and more on the inner level could be efficient and that relatively small numbers of samples can sometimes be used. Several remaining areas for methodological development are set out. Wider application of partial EVPI is recommended both for greater understanding of decision uncertainty and for analysing research priorities.
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