In this course we will give an overview of multiple testing issues and adaptive designs in trials. Participants will learn about a variety of new adaptive clinical trial designs, including multi-arm multi-stage (MAMS), platform trials, and adaptive randomisation.

As well as learning about how to design adaptive trials we will cover the implications of the design on the analysis and practical aspects of running the trial.

Throughout the course, real examples will be used to demonstrate the methodology. There will be a strong emphasis on learning how to use available software (in both R and Stata) to implement the methodology in practice.

https://www.newcastle-biostatistics.com/courses/admtp/

Course tutors:

Dr Michael Grayling, Janssen
Dr David Robertson, MRC Biostatistics Unit, University of Cambridge
Professor James Wason, Newcastle University

A short training course enabling delegates to be trained to use the Autism Clinical Interview for Adults (ACIA) - a diagnostic assessment for people age 16+ that can be used in clinical services or research

Delegates will receive training in use and administration, a copy of the manual and ACIA subject and informant booklets

Following training, delegates will be able to use the ACIA in their diagnostic practice and/or research.

Please note that we cannot accept payment via invoice for the training sessions. 

Precision medicine is about going beyond assessing whether a new treatment works on average to predicting which subgroups of patients receive benefit and to what extent. When subgroups, often defined by biomarkers, genetic, phenotypic or psychosocial characteristics, are associated with a treatment’s efficacy or toxicity, precision medicine offers substantial advantages to patients, trial sponsors, and the wider healthcare system. However, a barrier to realising the promise of precision medicine is the inappropriate use of traditional clinical trial design and analysis, which rely on estimates of population-averaged effects.

In this course we introduce the concept of precision medicine and cover innovative approaches. This includes novel designs (biomarker-guided, basket, umbrella, adaptive signature, adaptive enrichment designs and Sequential Multiple Assignment Randomized Trials) as well as efficient analysis approaches (e.g. Bayesian hierarchical modelling, identifying patient subgroups using high-dimensional genetic data) to help estimate sub-group specific treatment effects. These approaches have all been developed to improve the power, quality of information, and patient benefit provided by clinical trials. Examples from a wide variety of therapeutic areas will be discussed, with implementation in R software. Perspectives will be given on the future development of design, conduct and analysis of clinical trials in the field.

Course tutors:
Dr Svetlana Cherlin, Newcastle University
Professor James Wason, Newcastle University
Dr Haiyan Zheng, University of Bath

The ability to utilise external information when designing and analysing clinical trials brings many benefits, such as maximising the evidence provided by the trial, reducing the sample size required (particularly important for rare disease trials) and improving the generalisability of trial results. This course provides participants with a range of the latest statistical methods that can be used to incorporate external information and thus improve the efficiency and robustness of clinical trials. We also cover potential biases and pitfalls that may arise, and how to address them.

The following topics will be included:

• Bayesian methods that form prior distributions from elicited and (multiple) external data sources.
• Bayesian and hybrid approaches (e.g. assurance) that account for uncertainty in sample size calculations.
• Methods that facilitate borrowing of historical information or data from within the same trial (e.g. master protocols).
• Frequentist methods (e.g. propensity score weighting) that use external data, such as cohort studies and routinely collected healthcare records, to form synthetic control groups and generalise results from less representative trials to wider patient populations.
• Application to real clinical trials, including trials for rare diseases.

As well as the necessary theory, we will cover computational approaches to implement the methods and practical issues, such as funder and regulator views.

Course Tutors:
Professor James Wason, Biostatistics Research Group, Newcastle University
Dr Faye Williamson, Biostatistics Research Group, Newcastle University
Dr Kevin Wilson, School of Mathematics, Statistics & Physics, Newcastle University