Stochastic Cost-Effectiveness Analysis: A Simultaneous Marginal-Effect Approach

Authors


Address correspondence to: Gordon Liu, PhD, Associate Professor, Pharmaceutical Policy and Evaluative Sciences, University of North Carolina, Chapel Hill, NC 27516–7360. E-mail ggliu@unc.edu.

Abstract

Objective: The purpose of this study is to develop a cost-effectiveness methodology in the context of a simultaneous modeling framework that provides consistent point and interval estimates.

Methods: A simultaneous model of cost and effectiveness functions was developed to measure the incremental cost-effectiveness ratio for competing medical interventions. A feasible nonlinear least-squares method was suggested to estimate the simultaneous model. Using a series of hypothetical data, a simulation analysis was performed to show the superior performance of the proposed model, relative to the average-effect model, a widely used approach to cost-effectiveness estimation.

Results: The traditional average-effect approach has two shortcomings. First, it assumes two strong conditions: truly random distributions of all the significant nontreatment variables (both observed and unobserved) across study groups, and the independence of cost and effectiveness variables. Second, it does not give the confidence interval, an important measure to assess the stochastic nature and robustness of point estimates. In contrast, the simultaneous modeling approach provides marginal-effect estimates, imposing no restrictions on the random distributions of the individual characteristics across study groups. Furthermore, it takes into account the simultaneity of cost and effectiveness functions being estimated. The simulation analysis showed that the simultaneous modeling approach is significantly more unbiased and efficient in predicting the true cost-effectiveness ratio.

Conclusion: The simultaneous modeling approach is superior to the average-effect approach in the estimation of incremental cost-effectiveness ratios using data with significant nontreatment confounding factors. The advantages of the simultaneous modeling approach are particularly appealing for evaluative studies dealing with large-scale retrospective data at the patient level.

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