Clinical trials are increasingly expensive, while all too often not representative of the target patient group. Those with comorbidities are often excluded with the aim of simplifying trial design, even though they make up a significant proportion of the target group for the therapeutic course, and often those where it provides the greatest benefit.
Through real world evidence combined with simulated trials, we help to identify patients with highest unmet needs most likely to yield trial results sooner enabling more efficient trials and bringing therapeutics to patients earlier.
How we can help
With the increasing cost of clinical trials and the growing complexity of illness within target patient groups, trial design has a pivotal role in maximising the benefit and success of new therapeutics.
We bring together the latest real world data sets to give a clear understanding of target populations and design the recruitment criteria that will allow the trial to provide the strongest evidence base for health assessments.
- Understanding the co-morbidity profile of the target population. By 2035, approximately 17% of the UK population is projected to have four or more chronic conditions, so excluding these patients is not an option. We identify the key co-morbidity groups that will benefit from the therapeutics.
- Defining the recruitment criteria to achieve a representative trial population. Real world data sets provide a clearer picture of the target patient population capturing demographic, physiological, genetic and clinical risk factors. Combining this with established risk prediction models we help refine recruitment criteria to ensure the most effective use of patient and trial time.
- Trial simulation. With the target population defined only full trial simulation can ensure that the statistical robustness of results can be achieved across all patient groups.
- Trial extension simulation. While trials capture some outcomes, many others take years to capture. We develop simulated extended trials to capture the wider and longer-term impacts of interventions and translate this into population level impacts.
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