Industry's perception of presenting pharmacoeconomic models to managed care organizations

Previous research has shown that pharmacoeconomic (PE) data are considered important but may not be optimally utilized by decision makers. No research has compared the effectiveness of different types of PE models.

The purpose of this study was to examine the perceived value and understanding of PE models among decision makers in managed care organizations. The perspective of this study was from research scientists working in the pharmaceutical industry who present PE models to managed care clients. The study objectives were to (1) examine what types of models are best received by decision makers, (2) investigate the barriers to using PE models, and (3) recommend methods for improving PE models.

A telephone survey of 39 items was conducted with 20 PE research scientists from various U.S. pharmaceutical and biotechnology companies. Topics addressed included factors contributing to how well PE models are received, barriers to using PE models, and recommendations for improving PE models.

Models have an impact on health policy decision making. Nineteen of 20 respondents had at least one experience where a PE model played a role in optimizing the formulary positioning of a product. No single model format (e.g., decision analytic tools, spreadsheet analyses, Markov models, multivariate regression models) was regarded as the most effective model type. Although 7 of 20 respondents said simple spreadsheet models were most effective, well-designed, scientifically sound regression models were also reported to be very effective.

The respondents commonly used models to share PE information, which was said to play a role in making health policy decisions by decision makers in managed care. There was no consensus regarding the type of model that was most effective. Study participants indicated that a variety of model designs are effective, ranging from simple spreadsheet models to multivariate regression models. Recommendations for improving PE models include (1) producing scientifically sound models, (2) customizing models where possible, (3) making models transparent, (4) making models user friendly, and (5) involving a nonbiased third party for model development.