From trial and error to trial simulation. Part 1: the importance of model-based drug development for antidepressant...

Clinical trial simulation (CTS) allows the investigation of the influence of design characteristics on important aspects of clinical trials such as power and type I error. Simulation scenarios may be critical to decision making and prevention of study failure. The analysis and simulation of clinical trials in depression have, however, suffered from a lack of disease/dropout models that describe the individual time course of the clinical scale of interest. We propose a new model with dual random effects (DREM) derived from functional data analysis, which provides unbiased estimates of parameters and is suitable for the purposes of clinical trial simulation. A comparison of model performance is presented, along with standard statistical methods using various goodness-of-fit criteria. Our results show that data simulated using the DREM closely match individual patient data, including real-life dropout scenarios. In addition, parameterization in terms of interindividual variability ensures easier explanation of findings to clinical scientists, who ultimately make the relevant decisions.