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The Application of Quantitative Trade-Off Analysis to Guide Efficient Medicine Use

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The Application of Quantitative Trade-Off Analysis to Guide Efficient Medicine Use The application of quantitative trade-off analysis to guide efficient medicine use within the UK NHS by Pritesh Naresh Bodalia MPharm., PgCertPP., PgDipGPP., MSc., MRPharmS. A thesis submitted for the award of PhD Faculty of Population Health Sciences Institute of Epidemiology and Health Care University College London March 2018 I, Pritesh Naresh Bodalia confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Abstract Abstract 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. - 3 - Impact Statement Impact Statement The UK National Health Service (NHS) currently spends 15% of its £116 billion budget per annum on medicines; this is in excess of £17 billion, having grown from £13 billion in 2011-12. To ensure that the most effective medicines are available to patients, provider Acute Trusts have established local and / or regional Drugs and Therapeutics Committees (DTCs) to maintain a local formulary. Despite being the gold standard in evidence based medicine, randomised controlled trials (RCT) are designed to meet regulatory standards; therefore it has limited applicability in guiding clinical practice, particularly when multiple agents from the same therapeutic class are available. Guidance published by the National Institute for Health and Clinical Excellence (NICE) determines the clinical and cost-effectiveness of each individual new medicine, where the output is either the recommendation or non- recommendation of said medicine; where it is recommended it has to be made available for prescribing among other recommendations. Establishing the place in therapy for new and / or existing treatment as part of the DTC review is therefore often unclear. The UCL Research Department of Epidemiology & Public Health aim to provide leading and multi-disciplinary research with impact on real-world policy and practice. The quantitative trade-off analysis is a concept that gives consideration to key efficacy versus acceptability versus cost parameters for a range of similar medicines licensed for a given therapeutic indication, and aims to provide clarity to clinicians and guideline developers where multiple options exist. The methodology employed in order to undertake this analysis is based on Bayesian statistics, using network meta-analysis (NMA) techniques, via the WinBUGS software. Specific therapeutics areas explored include: refractory epilepsy; cardiovascular disease (hypertension and heart failure); overactive bladder syndrome; and atrial fibrillation. The research themes and projects were conceived in conjunction with a multi- disciplinary Area Prescribing Committee (regional DTC) within London and made possible via effective collaboration across a number of academic organisations including University College London, UCL Hospitals, University of Bristol, and the London School of Hygiene and Tropical Medicine. Specific areas of collaboration included individuals with a background in statistics - 4 - Impact Statement modelling and methodology, clinical / medical, health economics, and patient partnerships. The application of the quantitative trade-off analysis has successfully aided in the establishment of a hierarchy of treatments where regulatory data could not, and has been used to change practice across a pilot area of the UK NHS resulting in cost savings or cost-avoidance without compromising on patient care. Indirect comparisons, generated through NMA as conducted across a number of clinical specialities, not only helped develop rational treatment hierarchies, but also provide guidance on the choice of high priority comparator agents for direct head-to-head analysis in the form of an RCT. This approach is recommended as an extension of existing methods required by regulatory agencies in the assessment and licensing of new medicines. The outputs of the analyses conducted have been published in leading medical journals to inform wider practices. - 5 - Acknowledgements Acknowledgements I am grateful to Prof Aroon Hingorani and Prof Juan-Pablo Casas for their supervision, time and input. Similarly, Prof Raymond MacAllister and Dr Anthony Grosso have been invaluable in their assistance and enthusiasm throughout. I am also particularly grateful to Dr Robert Urquhart and Mr Ashik Shah for their continued mentorship for my research and development activities. I would also like to thank Mr David Wonderling for his advice and tuition on the use of WinBUGS in order to perform the Bayesian Network Meta-Analyses within this thesis and Dr Nicola Welton for her advice and support on developing techniques to the standard of the NICE [Technical] Decision Support Unit. In addition, I am indebted to Dr Michael Scott and the health economics team at University of Bristol for their contribution to the thesis in performing cost-utility analyses. I offer thanks to Prof Jonathan Sterne for leading the NIHR project which forms a section of this thesis, and to my teams, both within UCL Hospitals Pharmacy and from the NCL Joint Formulary Committee, for their support and understanding. I am thankful to the numerous experienced academics who have given time to perceptively critique this research, including the unknown peer-reviewers selected by the medical journal editors. Finally, I am, as ever, grateful to my family, wife and two boys for their continued encouragement without exception. - 6 - Table of Contents Table of Contents ABSTRACT............................................................................................................................... 3 IMPACT STATEMENT ............................................................................................................. 4 ACKNOWLEDGEMENTS ......................................................................................................... 6 TABLE OF CONTENTS ........................................................................................................... 7 LIST OF ABBREVIATIONS....................................................................................................13 LIST OF FIGURES .................................................................................................................16 LIST OF TABLES ...................................................................................................................21 1.0 CHAPTER 1: INTRODUCTION ..................................................................................25 1.1 INTRODUCTORY STATEMENT ......................................................................................25 1.2 DRUG APPRAISAL PROCESS IN CLINICAL TRIALS .........................................................26 1.2.1 Phase I clinical trials ........................................................................................26 1.2.2 Phase II clinical trials .......................................................................................27 1.2.3 Phase III clinical trials ......................................................................................27 1.2.4 Phase IV clinical trials .....................................................................................27 1.3 PHARMACEUTICAL INDUSTRY .....................................................................................28 1.3.1 Design Bias .....................................................................................................28 1.3.2 Data Interpretation...........................................................................................29
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