The expected value of partial perfect information (EVPPI) is a theoretically justifiable and informative measure of uncertainty in decision-analytic cost-effectiveness models, but its calculation is computationally intensive because it generally requires two-level Monte Carlo simulation. We introduce an efficient, one-level simulation method for the calculation of single-parameter EVPPI.
We show that under mild regularity assumptions, the expectation-maximization-expectation sequence in EVPPI calculation can be transformed into an expectation-maximization-maximization sequence. By doing so, calculations can be performed in a single-step expectation by using data generated for probabilistic sensitivity analysis. We prove that the proposed estimator of EVPPI converges in probability to the true EVPPI.
The performance of the new method was empirically demonstrated by using three exemplary decision models. Our proposed method seems to achieve remarkably higher accuracy than the two-level method with a fraction of its computation costs, though the achievement in accuracy was not uniform and varied across the parameters of the models. Software is provided to calculate single-parameter EVPPI based on the probabilistic sensitivity analysis data.
The new method, though applicable only to single-parameter EVPPI, is fast, accurate, and easy to implement. Further research is needed to evaluate the performance of this method in more complex scenarios and to extend such a concept to similar measures of decision uncertainty.