According to the research done by Di Masi et al. (2003) 800 million dollar is needed in order to have a new entity reach the market. Another source from the Investor’s Business Daily (2007) stated that the amount spent is actually 1 billion dollar. The years spent is estimated to be around 15 years. Many attention is now focused to studies that reduce the cost and the time. Adaptive design is a solution found to this problem, where modification is done in the middle of the trial based on the information accrued. Adaptive design can be applied using both the frequentist and Bayesian method. The intention of the authors of this paper, Spann et al. (2008 ) is to:-
1. Apply Bayesian adaptive to a case study
2. Demonstrate the benefit of it and provide advance understanding of using this approach in regulatory environment.
The authors focused on phase III study, in a trial for patients who had DSM-IV diagnosis of schizophrenia and related disease. The methods advocated here follow all aspects of trial protocol except that a Bayesian approach is employed in the analyses, decision criteria and treatment allocation which is based on predictive probability of treatment response without the side effect. The prior was obtained by combining historical pharmacokinetics data and expert opinon. Patients were randomized first according to the ratio 2:2:1 and then Bayesian adaptive is applied to assign treatment to patients. To assign this treatment, it depends on the calculated joint predictive probability of treatment safe and patient response. This method will reduce patients’ exposure to ineffective treatments or treatments with adverse side effects. MCMC was used to draw since the posterior distribution did not have a closed form.
The authors reached the same conclusion to the analysis previously done, except that they could stop early for efficacy. They required only a half number of patients from the original frequentist study. Since the trial completed earlier, they estimated that 40% of original time and 1 million dollar were saved. Sensitivity of the design with respect to prior distributions was investigated by simulation. If informative priors were used with respect to simulated data, then power is 100% for rejecting false null hypothesis. On the other hand if noninformative priors with respect to simulated data were used, power can be achieved up to 79%.
The authors believed that adaptive design is a favorable approach to ensure that the new entity reach the market sooner and people can obtain the medicine at a lower cost. At the end of this paper, Spann et al. (2008 ) wrote a bit on the difference of frequentist and Bayesian ideas.
