Evaluation of treatment response in depression studies using a Bayesian parametric cure rate model.

Efficacy trials with antidepressant drugs often fail to show significant treatment effect even though efficacious treatments are investigated. This failure can, amongst other factors, be attributed to the lack of sensitivity of the statistical method as well as of the endpoints to pharmacological activity. For regulatory purposes the most widely used efficacy endpoint is still the mean change in HAM-D score at the end of the study, despite evidence from literature showing that the HAM-D scale might not be a sensitive tool to assess drug effect and that changes from baseline at the end of treatment may not reflect the extent of response. In the current study, we evaluate the prospect of applying a Bayesian parametric cure rate model (CRM) to analyse antidepressant effect in efficacy trials with paroxetine. The model is based on a survival approach, which allows for a fraction of surviving patients indefinitely after completion of treatment. Data was extracted from GlaxoSmithKline's clinical databases. Response was defined as a 50% change from baseline HAM-D at any assessment time after start of therapy. Survival times were described by a log-normal distribution and drug effect was parameterised as a covariate on the fraction of non-responders. The model was able to fit the data from different studies accurately and results show that response to treatment does not lag for two weeks, as is mythically believed. In conclusion, we demonstrate how parameterisation of a survival model can be used to characterise treatment response in depression trials. The method contrasts with the long-established snapshot on changes from baseline, as it incorporates the time course of response throughout treatment.