Partial expected value of perfect information (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 article examines the computation of partial EVPI estimates via Monte Carlo sampling algorithms. The mathematical definition shows 2 nested expectations, which must be evaluated separately because of the need to compute a maximum between them. A generalized 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 considered. Maxima of Monte Carlo estimates of expectations are biased upward, and the authors show that the use of small samples results in biased EVPI estimates. Three case studies illustrate 1) the bias due to maximization and also the inaccuracy of shortcut algorithms 2) when correlated variables are present and 3) when there is nonlinearity in net benefit functions. If relatively small correlation or nonlinearity is present, then the shortcut algorithm can be substantially inaccurate. Empirical investigation of the numbers of Monte Carlo samples suggests 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. A wider application of partial EVPI is recommended both for greater understanding of decision uncertainty and for analyzing research priorities.

This updated analysis provides more certainty with regard to the potential cost-effectiveness of LBC compared with conventional Pap smear testing. However, there is uncertainty regarding the relative effectiveness (and cost-effectiveness) of the two main LBC techniques. Further research in the area of utility assessment may be worthwhile and possibly a full cost-effectiveness study of LBC based on a trial of its introduction in a low-prevalence population, although the results of the modelling analysis provide a robust argument that LBC is a cost-effective alternative to conventional cervical cancer screening. A randomised comparison of the two main techniques may also be useful.

Objective To evaluate the cost effectiveness of four disease modifying treatments (interferon betas and glatiramer acetate) for relapsing remitting and secondary progressive multiple sclerosis in the United Kingdom. Design Modelling cost effectiveness. Setting UK NHS. Participants Patients with relapsing remitting multiple sclerosis and secondary progressive multiple sclerosis. Main outcome measures Cost per quality adjusted life year gained. Results The base case cost per quality adjusted life year gained by using any of the four treatments ranged from £42 000 ($66 469; 61 630) to £98 000 based on efficacy information in the public domain. Uncertainty analysis suggests that the probability of any of these treatments having a cost effectiveness better than £20 000 at 20 years is below 20%. The key determinants of cost effectiveness were the time horizon, the progression of patients after stopping treatment, differential discount rates, and the price of the treatments. Conclusions Cost effectiveness varied markedly between the interventions. Uncertainty around point estimates was substantial. This uncertainty could be reduced by conducting research on the true magnitude of the effect of these drugs, the progression of patients after stopping treatment, the costs of care, and the quality of life of the patients. Price was the key modifiable determinant of the cost effectiveness of these treatments.

Strategies which combine different screening modalities may provide greater clinical and economic benefits. The collection of comprehensive screening data using a uniform format will enable comparative analysis across screening programmes in different countries, will improve our understanding of the disease and will allow identification of optimal screening modalities.

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