Innovations in monitoring and data quality control in clinical trials

Clinical trials serve a key role in drug development by providing scientific knowledge on the risks and benefits of medical treatments or therapies. Conducting a clinical trial is a time-consuming endeavor which typically requires a large financial budget. It is therefore important to critically reflect on how resources are being spent. Indeed, the efficiency of clinical trial conduct is becoming an increasingly active topic of research and discussion, in view of rising healthcare costs. Estimates suggests that a considerable portion (15 to 30 percent) of a phase III clinical trial budget is spent on the monitoring of the local investigators (i.e. the treating physicians) and their teams. Traditionally, this is the responsibility of a Clinical Research Associate (CRA), who is employed by the sponsor or contract research organization. The CRA visits the local centers on regular intervals and spends a large portion of his/her time comparing all submitted data against the source data, correcting any discrepancy that is detected. This thesis examines and further develops alternative monitoring and trial management strategies which are aimed to conduct clinical trials more efficiently, while at the same time preserving or even improving data quality. Mainly, it focuses on methods that reduce the reliance on the physical presence of the CRA on the local centers and make better use of centrally available data, and it reflects on specific aspects of trial management for which the effectiveness has been the subject of discussion. After a general introduction, the thesis first focuses on the use of statistical sampling methodology to reduce the effort of source data verification. Next, it examines how central statistical monitoring can be used for the detection of data fabrication (i.e. possible fraud). Third, to improve selection and management of clinical trial sites, it is evaluated to which extent center-level information can predict their performance in terms of meeting recruitment targets. Furthermore, the impact of outcome misclassification is assessed, and the value of so-called adjudication to overcome this problem is critically evaluated. As a last topic, the thesis addresses application of so-called ‘risk-based monitoring’ procedures in the context of pragmatic trials. The thesis ends with a general discussion and framework on the topic of clinical trial monitoring