The application of quantitative trade-off analysis to guide efficient medicine use within the UK NHS

• Main Author: Bodalia, Pritesh Naresh
• Published: University College London (University of London) 2018
• Online Access: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.747564

Background: The UK National Health Service (NHS) currently spends in excess of £17 billion per annum on medicines. To ensure sustainability, medicines with limited clinical value should not be routinely prescribed. The randomised controlled trial (RCT) aims to meet regulatory standards, therefore it has limited value to guide practice when multiple agents from the same therapeutic class are available. The quantitative trade-off analysis, with consideration to efficacy, safety, tolerability and / or cost, may enable the translation of regulatory data to clinically-relevant data for new and existing medicines. / Methods: This concept aims to provide clarity to clinicians and guideline developers on the efficient use of new medicines where multiple options exist for the same licensed indication. Research projects of clinical relevance were identified from an academically led London Area Prescribing Committee (APC). Therapeutic areas included refractory epilepsy, hypertension and heart failure, overactive bladder syndrome, and atrial fibrillation. Frequentist and / or Bayesian meta-analysis were performed and supplemented with a trade-off analysis with parameters determined in consultation with field experts. / Results: The trade-off analysis was able to establish a rank order of treatments considered thereby providing clarification to decision-makers on the DTC / APC panel where regulatory data could not. The results, presented as a hierarchy of treatments, enabled modifications to prescribing trends within North Central London as the pilot site, resulting in significant cost avoidance and cost savings for the NHS. / Conclusions: The quantitative trade-off analysis was able to resolve concerns raised by the DTC / APC panel via translation of regulatory data to clinically-relevant data with consideration to defined benefits and harms. Results were implemented successfully within a local pilot health economy. This approach is recommended as an extension of existing methods required by regulatory agencies in the assessment and licensing of new medicines.