Many confirmatory clinical trials turn out to be negative and do not lead to the registration of a new drug or the introduction of a new treatment. Consequently, there is growing interest amongst clinical researchers in the use of efficient methods for conducting early and late phase trials to identify the most promising compounds, and in using interim analyses to allow trials to be stopped as soon as there is sufficient evidence to reach a convincing conclusion.

Adaptive designs allow mid-trial modifications to be made to the trial so that the accumulating data guides investigators to the most appropriate form of evaluation. While offering great flexibility during the trial, they require specialised approaches to be designed and analysed.

Dates
Tuesday 17th of September – Thursday 19th of September 2024 (3 full days)

University of Cambridge Students and Staff, whose department will be covering the participation cost, please contact Admin Team at [email protected] to arrange an internal crosscharge and obtain an access passcode to use this booking system. PO number and Access Passcode is required during the booking process.

Recent high-profile clinical trials have used a response-adaptive procedure as the way to implement an “adaptive” experiment. Such response-adaptive designs allow a variety of mid-trial modifications to be made to the trial so that the accumulating data guides investigators to the most appropriate form of evaluation (e.g. they can be used for arm selection, arm dropping, or simply to assign more patients on average to a superior treatment where possible). While offering great flexibility during the trial, they require specialised approaches for both the trial design and analysis.

Consequently, there is growing interest amongst clinical researchers and the clinical trials community in the use of these methods for conducting either early or late phase trials that more efficiently or more accurately balance competing goals, while gathering sufficient evidence to reach a convincing conclusion.

University of Cambridge Students and Staff, whose department will be covering the participation cost, please contact Admin Team at [email protected] to arrange an internal crosscharge and obtain an access passcode to use this booking system. PO number and Access Passcode is required during the booking process.

The course comprises four half-days’ worth of content plus the final hackathon, and will take place over 3 weeks (plus a preliminary week 0). It consists of some on-demand pre-recorded content and some timetabled (live) content. The first day of Week 1 will be Monday 4th November 2024. All the core content of the course is pre-recorded - live sessions are not compulsory to attend, but are supplementary to the core content. They represent a chance to engage with the course tutors. Several of the live sessions will be recorded.

Each half-day can be done whenever is convenient. A half-day of content includes three pre-recorded talks (around 20-30 minutes each) and one practical session (around 1-1.5 hour). Each of the three computer practicals should be performed individually, but there is an associated live drop-in session to come and ask questions. There is also a recorded debrief session that runs through the practical content. In terms of live content, in addition to the practical drop-in sessions, paper discussion, and hackathon, there is also a question and answer session each week to ask your questions. Questions can be asked during the week on a dedicated Slack channel, or you can ask questions live (Q+A sessions will be recorded).

University of Cambridge Students and Staff, whose department will be covering the participation cost, please contact Admin Team at [email protected] to arrange an internal crosscharge and obtain an access passcode to use this booking system. PO number and Access Passcode is required during the booking process.

The majority of biologic and small molecule drugs perturb protein targets to exert their effects. With the recent explosion in the availability of large-scale genetic association data, it is increasingly feasible to identify genetic variants that proxy the effect of perturbing a protein drug target. Leveraging such genetic data thus offers an efficient and cost-effective approach for identifying drug targets and studying their effects.

This short course “Genetics in drug development” will provide theoretical and practical advice on using genetic data to:
1.    Identify drug targets
2.    Investigate the effects of drug target perturbation for different indications
3.    Explore potential adverse effects and repurposing potential for drug targets
4.    Identify biomarkers to explore drug efficacy in early stage clinical trials
5.    Identify population subgroups for which drug effects may vary
6.    Explore interactions between different drugs and exposures

University of Cambridge Students and Staff, whose department will be covering the participation cost, please contact Admin Team at [email protected] to arrange an internal crosscharge and obtain an access passcode to use this booking system. PO number and Access Passcode is required during the booking process.

In many biomedical applications, there is increasing interest in modelling within-individual variability of health measures recorded over time to characterise fluctuations around the mean trajectory. Simple summary statistics (like the standard deviation of the observation for each individual) do not account for the imbalance in the longitudinal observations across individuals and their time dependency, therefore statistical models for the within-individual variability are needed. 

In this course, we are going to introduce some statistical models that can be used for the quantification of within-individual variability. The first session will be devoted to models for longitudinal data only, while the second will cover a basic introduction of joint modelling for longitudinal and time-to-event data, with an extension to within-individual variability.  

The course compliments the workshop on within-individual variability taking place in Cambridge on Thursday 26th September. Details here. 

Please check 'More Info' tab for the programme and pre-requisites.

University of Cambridge Students and Staff, whose department will be covering the participation cost, please raise a Purchase Order (or contact a member of your department who can assist with this) and send a PDF of the Purchase Order to [email protected] . You will then be sent the passcode which you will need to use along with the Purchase Order number to complete registration below, selecting the 'UoC Student/Staff Registration' option. 

Course aims
This short course introduces students to Bayesian statistical methods in biomedical settings, and provides skills for designing, assessing and interpreting Bayesian analyses using the R and JAGS statistical software. The emphasis throughout will be on practical, applied modelling: code to carry out analyses will be provided.

Course outline
The course runs on the Moodle online learning platform, and involves 7 sessions:
1.    Quantifying uncertainty with probability
2.    Bayesian inference
3.    Bayesian regression models
4.    Critiquing and comparing Bayesian models
5.    Hierarchical models
6.    Modelling with missing and censored data
7.    Integrating multiple sources of data

Course delivery
The course will be delivered via the Moodle online learning platform. The course consists of 7 half-days worth of content, and will take place over 7 half-days across 3 weeks starting on Friday 15th November 2024.
It will consist of some on-demand content and some timetabled (live) sessions.

University of Cambridge Students and Staff, whose department will be covering the participation cost, please contact Admin Team at [email protected] to arrange an internal crosscharge and obtain an access passcode to use this booking system. PO number and Access Passcode is required during the booking process.