The Health Economics & Health Technology Assessment (HEHTA) research group at the University of Glasgow has some spaces remaining on its live online course ***Maximising the Value of Clinical Trial Data: Advanced Analysis for Economic Evaluation and Modelling*** 

This course is a natural follow-on from advanced modelling courses and covers the fundamental concepts and practice of the key advanced analytic techniques that are required when determining appropriate approaches and estimating parameter values for cost-effectiveness models. It is also highly relevant to those seeking to improve the relevance of trial-based cost-effectiveness. It will be held using a blend of live, online lectures and tutorials.

The course is led by our Professor of Medical Statistics James Lewsey and includes sessions from notable experts including Professor Neil Hawkins & Mr Andrew Davies (University of Glasgow), Dr Suzanne Freeman (University of Leicester) and Professor Andrew Briggs (London School of Hygiene & Tropical Medicine).

The course will cover:

 - Regression analysis to estimate costs and utilities based on trial data, including the use of appropriate techniques for analysing panel data with repeated measures per patient, dealing with differential follow-up, censoring, missing data, and skewed data

 - Survival analysis techniques including the use of survival analysis, competing risks analysis, cure models, and multi-state modelling

 - Treatment switching techniques such as rank-preserving structural failure time (RPSFMT), inverse probability of censoring weights (IPCW) and two-stage estimation models.

The course will be run over eight half-day, live online sessions on the following dates:

Week 1: 3rd - 5th November 2021
 
Week 2: 10th - 12th November 2021
 
Week 3: 17th & 18th November 2021
** Discounts are available for public institutions, groups, and participants from LMICs **

To enquire, please email [log in para visualizar] or visit our website to book your place:https://bit.ly/3du7SUl. Please book ASAP to avoid disappointment.