World Congress EPA 2025 5-6 March
The Visible Analytics team is excited to exhibit, present, and engage at the World Evidence, Pricing and Access Congress 2025 in Amsterdam. Join us and learn how we use strategic insights and expert analytics to help pharmaceutical and medical device companies navigate our changing and challenging HTA landscapes.
Come meet us at our booth #56, join our HTA session and ask us about our ML-NMR poster presentation!
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Wednesday 5 March
How well do you know your patients? Optimising collection of patient characteristics to support HTA
Presenter: Dr. Edit Remak, Partner and Director of Health Economics, Visible Analytics, Budapest, Hungary
All trials collect information on baseline patient characteristics to be able to assess the success of randomisation. If important baseline factors appear well balanced, it is likely that any differences in outcome between intervention and control groups can be attributed to the treatment. Furthermore, knowing the baseline characteristics of the trial participants allows assessment of how closely these match patients seen in clinical practice, and therefore how generalisable the results of the trial will be. Although randomisation only ensures balance of patient characteristics at baseline, most trials fail to collect information on patient characteristics beyond the active treatment period. However, health technology assessments (HTA) require information on all time periods where the treatment may influence health or cost outcomes which often go beyond the active treatment period.
The session presents a series of case studies highlighting the importance of early planning for data collection in clinical trials and RWE with HTA in mind from the early stages:
- Collecting patient characteristics information at timepoints influencing outcomes beyond the active treatment period:
- For better cross-over adjustment
- For better understanding of impact of subsequent treatments
- Collecting patient characteristics that are not directly relevant for technology of interest, but are for e.g. a comparator
- For better comparability to competitor RCTs
- For better ability to match to RWE
- Overall better strategy to what information is being collected at what time-points to inform novel methods of indirect treatment comparisons such as the multilevel network meta-regression (ML-NMR) approaches which can be used to estimate relative treatment effects in multiple target populations as required for JCA
Poster presentation
Yuxian Chen MSc BSc1, Anastasiya Armitage MSc BSc1, Louise Linsell DPhil MSc BSc1, Keith Abrams PhD MSc BSc1
1 Visible Analytics, Oxford, UK
