Our essential task is defining these elements, and the statistics to support the decision process will naturally follow. Our plan is to identify real decisions and real decision makers, and make explicit the resources invested, as well as the losses, and the gains to be realized. Just getting analysts and decision-makers to think of these as problems in valuation of outcomes, and not problems merely in processing of statistical data, would be a step forward. Thus, the more difficult challenge will likely be developing the values side of the decision problem rather than the probability side. First, for virtually the first time, the industry brought experimental data to the field of cost-effectiveness research.
Uncertainty abounds in such studies and was handled through sensitivity analysis.
The decision-analytic models were, in essence, an informal application of Bayesian reasoning. When even expert opinion was not sufficient, techniques such as threshold analysis were used to determine the impact of outer boundaries of reality. The second thing that happened as a result of economic evaluation being included within drug development was that standards for accepting the validity of cost-effectiveness results, as well as the paradigm itself, changed dramatically. This situation has created frustration and has led to at least two problems. Few empirical, experimental studies find statistically significant economic results even when large differences in means are present and when accompanied by strong clinical and economic hypotheses.