An Efficient Estimator for the Expected Value of Sample Information.

Abstract

Conventional estimators for the expected value of sample information (EVSI) are computationally expensive or limited to specific analytic scenarios. I describe a novel approach that allows efficient EVSI computation for a wide range of study designs and is applicable to models of arbitrary complexity. The posterior parameter distribution produced by a hypothetical study is estimated by reweighting existing draws from the prior distribution. EVSI can then be estimated using a conventional probabilistic sensitivity analysis, with no further model evaluations and with a simple sequence of calculations (Algorithm 1). A refinement to this approach (Algorithm 2) uses smoothing techniques to improve accuracy. Algorithm performance was compared with the conventional EVSI estimator (2-level Monte Carlo integration) and an alternative developed by Brennan and Kharroubi (BK), in a cost-effectiveness case study. Compared with the conventional estimator, Algorithm 2 exhibited a root mean square error (RMSE) 8%-17% lower, with far fewer model evaluations (3-4 orders of magnitude). Algorithm 1 produced results similar to those of the conventional estimator when study evidence was weak but underestimated EVSI when study evidence was strong. Compared with the BK estimator, the proposed algorithms reduced RSME by 18%-38% in most analytic scenarios, with 40 times fewer model evaluations. Algorithm 1 performed poorly in the context of strong study evidence. All methods were sensitive to the number of samples in the outer loop of the simulation. The proposed algorithms remove two major challenges for estimating EVSI--the difficulty of estimating the posterior parameter distribution given hypothetical study data and the need for many model evaluations to obtain stable and unbiased results. These approaches make EVSI estimation feasible for a wide range of analytic scenarios.