Optimal design of clinical trials with computer simulation based on results of earlier trials, illustrated with a lipodystrophy trial in HIV patients

The designer of a clinical trial needs to make many assumptions about real-life practice based on prior knowledge. Simulation allows us to learn from experience by using the information obtained from a trial to improve the original estimators of population parameters. We propose using data from a previous trial to formulate assumptions that can be used to simulate trials and thus improve the design of new trials. To demonstrate our method, we used data from a real clinical trial which had been designed to evaluate cholesterol level changes as a surrogate marker for lipodystrophy in HIV patients. We were able to identify the optimal design that would have minimised the cost of a trial subject to a statistical power constraint which could then be used to design a new trial. In particular, we focused on three factors: the distribution of cholesterol levels in HIV patients, trial recruitment rates and trial dropout rates. We were able to verify our hypothesis that the total cost resulting from carrying out a clinical trial can be minimised by applying simulation models as an alternative to conventional approaches. In our findings the simulation model proved to be very intuitive and a useful method for testing the performance of investigators' assumptions and generating an optimal clinical trial design before being put into practice in the real world. In addition, we concluded that simulation models provide a more accurate determination of power than conventional approaches, thus minimising the total cost of clinical trials.