A priority of the Institute is the development of methods which have a direct impact on the design, conduct or analysis of our or other people’s studies. The MRC CTU at 911±¬ÁÏÍø is conducting the majority of the research into trials methodology, and their work is presented in three themes:
- Design of trials, meta-analyses and observational studies
- Multi-arm, multi-stage (MAMS) platform trials
- Designing phase II (and III trials) based on an enhanced decision process at the end of phase II
- Improving the design of stratified medicine trials and biomarker validation studies
- Designing trials in uncommon diseases
- Cluster randomised and stepped wedge trials
- A flexible framework for complex time-to-event outcome trials
- Planning and accounting for missing data
- Improving the analysis and design of trials with longitudinal data or clusters of varying size
- Designing trials with recurrent events as the primary outcome measure
- Re-randomising patients into trials
- Design, development and validation of prognostic models
- Effective and efficient conduct of trials and meta-analyses
Trial conduct methodology:
- Providing practical examples of how novel designs can be implemented
- Evaluating and implementing strategies to ensure that data on randomised patients is not lost through patient withdrawal
- Efficient trial monitoring
- Getting trials started more quickly, and facilitating prompt reporting of outcome data
Meta-analysis conduct methodology:
- Speeding up the evaluation of individual therapies in meta-analysis
- Providing tools and guidance to promote greater awareness, understanding and use of IPD meta-analysis
- Resolving outstanding issues in systematic review conduct
- Analysis of trials, meta-analyses and observational studies
- Analysing multi-arm multi-stage (MAMS) trials
- Analysing time-to-event outcomes
- Multivariable prognostic models and treatment-covariate interactions (including validation)
- Appropriate analysis of longitudinal and clustered data
- Causal models for answering questions not addressed by randomisation
- Missing data and improved sensitivity analysis for missing outcome data
- Design, development and validation of prognostic models