This PhD project at Newcastle University focuses on developing advanced statistical methodology for platform trials that use composite endpoints. Platform trials are an innovative approach in clinical research, allowing for the simultaneous evaluation of multiple interventions within a single, adaptive trial structure. These trials are increasingly used across various medical fields, especially where it is challenging to define a single primary endpoint. Instead, composite endpoints—such as Major Adverse Cardiovascular Events (MACE)—combine several clinically relevant outcomes into a single measure, providing a more comprehensive assessment of treatment effects.

The project addresses a key methodological gap: how to set appropriate stopping rules for interventions in platform trials when using advanced composite endpoint analysis methods. While traditional analyses often treat composite endpoints as binary, newer methods like the Win-ratio, Wei-Lachin test, and augmented binary method offer more nuanced insights but complicate interim decision-making. The student will first review and compare these methods, then derive the correlation between their test statistics at interim analyses within a group-sequential framework. This will enable the development of formulae to evaluate stopping probabilities, type I error rates, and statistical power.

Subsequent work will extend these methods to multi-arm trials, where several interventions are compared against a common control. The student will adapt existing two-arm methodologies to multi-arm settings, ensuring their applicability to the complex structure of platform trials. The final phase involves a comparative evaluation of the statistical properties of platform trials using different composite endpoint methods, culminating in practical guidance for their use in real-world studies.

The successful candidate will join the Biostatistics Research Group within the Population Health Sciences Institute at Newcastle University, benefiting from primary supervision by Professor James Wason and co-supervision from experts involved in multi-arm, multi-stage (MAMS) and platform studies. There are opportunities for placements at collaborating institutions, such as University College London (UCL), and for engagement with public and patient involvement (PPI) groups, enhancing both methodological and translational aspects of the research.

Applicants should have a strong background in statistics, mathematics, or a related quantitative discipline, with an interest in clinical trials and statistical methodology. The studentship is funded, likely covering tuition and stipend according to MRC Doctoral Training Partnership guidelines. The application deadline is 26 January 2026, and prospective applicants are encouraged to contact Professor James Wason ([email protected]) to discuss their interest before 2 January 2026. For full application details and documentation, visit the MRC DTP opportunities page.