We have expertise in developing and assessing the novel methodology that underpins innovative clinical trials. Our interests include medical and clinical trial ethics, use of qualitative and mixed methods, quantitative methods including clinical trial statistics and development of patient reported outcome measures (PROMs). Our methodological work informs guidelines and we work closely with our colleagues in Warwick Clinical Trials Unit and other institutions to bring novel methodology to practical application.
We have a research interest in the ethics of recruitment and consent processes for trials involving participants from vulnerable populations including trials in emergency care and in people with cognitive impairment. Trials that we have recently been involved with include DAPA and PARAMEDIC2.
Understanding how an intervention works, why it does not work and how it is experienced requires a mixed methods design combining data from observation and interviews with data captured as part of the trial and publicly available data. One example is the Chronic Headache Education and Self-management Study (CHESS) to investigate whether a new education and self-management programme will help improve quality of life for people living with chronic headaches.
The Statistics and Epidemiology group develop and apply innovative statistical methods for clinical trials with major research programmes in adaptive trial designs and analysis, clinical trials in small population groups and collaborate closely with Warwick Clinical Trials Unit. One of the trials we are involved with that incorporates the adaptive design is START:REACTS.
CHEW is working on new methods in a range of areas. These include: cost-effectiveness conditional power computations for stopping clinical trials early (as part of the START:REACTS trial); methods and strategies for trial-based economic evaluation of complex interventions in resource constrained settings (STREAM and MILESTONE trials) and within multi-arm multi-stage trials; and developing multivariate linear and generalised linear mixed-effects models for analysis of trial-based cost-effectiveness data.