The process of developing and testing a new treatment or intervention takes a long time, a lot of money, and often ends in failure. The high cost is primarily due to expensive clinical trials, which are required to show that the treatment is safe and effective. Improving the efficiency of clinical trials, to make maximum use of limited resources, is a priority research area. This research programme will develop statistical methodology to ensure that clinical trials have the tools necessary to cope with current and future challenges. The first area of focus is statistical methodology for best using the increasing amount of high-dimensional biomarker data in clinical trials. With increasing availability of routinely collected biomarker data, modern clinical trials urgently require suitable methods to incorporate them prospectively. Current approaches rely on less efficient statistical methods such as testing each variant one-by-one in a logistic regression, and classifying patients as high-risk if they are positive for a certain number of biomarker. By combining novel adaptive designs and state-of-the-art high-dimensional statistical methods such as Bayesian sparse regression we can improve on this. Through working with clinicians, this novel methodology will be available for use in real trials for areas such as cardiovascular diseases and oncology. A second area of focus is methodology for ongoing trials that test multiple treatments and biomarkers. In an ongoing trial new treatments and biomarkers are added in continually as treatments are found sufficiently promising to move to phase III trials, or are dropped due to lack of effectiveness. Some real trials are already doing this due to the considerable logistical and administrative advantages. This programme will focus on statistical issues in ongoing trials. This includes how to optimally choose decision criteria for dropping or progressing a drug to phase III and how to optimally plan phase III trials that result. Collaborations with a multidisciplinary set of statisticians and clinicians will allow us to implement developed methods in practice. A third area of focus is to improve the analysis of trials using composite endpoints. In this case it is possible to gain considerable power by fitting a suitable model to the data. These methods will be applied to a variety of disease areas including oncology trials, rheumatoid arthritis and lupus.