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Statistical Issues in Drug Licensing & Health Technology Assessment (HTA) Reimbursement

Collision or Convergence

3rd July 2023

What do the statisticians think?

Statistical methods are used in both HTA and regulatory drug licensing decision making. However, aspects of clinical trial design and analyses that aim to satisfy both objectives are not always aligned! This can have far reaching consequences for patients access and increasing costs of future Research & Development programmes.

Not being able to bridge the gap between the payer's and regulator's requirements can incur huge costs, impact the drug development process and delay patients' access to critical medicines.


How does R-S-S bridge the gap between divergent and convergent thinking?

R-S-S are at the forefront of providing cutting-edge data methods and solutions, helping clients overcome technical and difficult challenges, by navigating them through regulatory and HTA channels. Our technical capabilities using statistical and data sciences technology and expertise, combined with sound in-house regulatory experts allows us to provide real solutions to both regulatory AND reimbursement agencies – whether it is scientific advice, outcome qualification, economic modelling or network meta analyses (NMA). We guide our clients in a collaborative partnership, offering competitive and consistently high quality solutions to navigate through the HTA and drug licensing hurdles. Some of our technical solutions include electronic Clinical Outcome Assessment (eCOA), using machine learning (ML) and artificial intelligence (AI) systems to solve important health problems.


One of our innovations

R-S-S are pioneers in developing innovative tools using machine learning and AI to assist with licensing AND reimbursement. Our DM-RSS software is a first of its kind, a Decision Making Tool for Reimbursement. The sample size calculator enables you to design trials for cost-effectiveness while at the same time helping to identify subgroups which may not demonstrate cost-effectiveness, saving huge resources. At the same time, the easy functionality allows calculation of sample sizes in a Bayesian framework - which can be smaller than conventional methods. In orphan indications a few extra patients can add years to the filing process.

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