OHE presents a series of lunchtime seminars throughout the year. The most recent seminar, held in late April, considered the influence of cost-effectiveness and other factors on NICE decisions.
Multi criteria decision analysis for healthcareSunhong Kwon
Multi-criteria decision analysis (MCDA) is a process used to evaluate multiple criteria in healthcare decision making. Three key aspects of MCDA are summarized:
1. MCDA identifies criteria like clinical benefits, safety, costs, and other factors to evaluate healthcare interventions. Studies consider criteria like disease severity, population affected, clinical guidelines, and costs.
2. Interventions are measured and scored on each criterion using evidence and expert opinion. Scores are often on predefined scales.
3. Criteria weights reflecting importance are generated, commonly using analytic hierarchy process or defined scales. Weights involve multiple stakeholders.
Sensitivity analysis assesses uncertainty, though many studies do not report this. MCD
The document discusses the formation of an ISPOR task force to develop guidance on the use of multi-criteria decision analysis (MCDA) in healthcare decision making. The task force will define MCDA, identify different MCDA techniques, and provide guidance on which techniques are best suited for different types of healthcare decisions. The document also discusses proposed definitions of MCDA and debates the appropriate scope and focus of the task force's work.
The document summarizes Martina Garau's presentation on assessing the value of co-dependent technologies such as diagnostic tests and treatments. It discusses how co-dependent technologies can create additional value dimensions beyond traditional cost-effectiveness analysis, challenges to valuing and proving the benefits of these technologies, and examples from countries like Australia and the UK. The presentation analyzes frameworks for capturing different value elements, barriers to evidence generation, and proposes processes for jointly assessing diagnostics and treatments.
Technologies that enhance the precision and effect of therapies can make a critical contribution to ensuring value for money and improving patient care. Methods and processes for assessing value, however, still are imperfect. This presentation reviews the challenges and identifies some approaches for meeting them.
Adam Steventon: How can predictive risk models help?Nuffield Trust
This document discusses how predictive risk models can help evaluate interventions in observational studies. It notes challenges with observational studies, such as regression to the mean and lack of control groups. It proposes several solutions to address these challenges, including before-after studies, regression adjustment, matching controls, and regression discontinuity designs. Matched controls are described as a way to reduce dependence on regression model specification by "data pre-processing" to compare intervention patients to similar control patients. The document concludes by surveying the current state of telehealth studies, finding most are descriptive or use before-after designs, with few employing more rigorous controlled designs.
Martina Garau presented on using multi-criteria decision analysis (MCDA) to value orphan medicines. A pilot study developed a value framework with 10 attributes related to disease impact and treatment benefits. Expert and patient workshops then weighted the attributes and applied them to two case studies, finding treatment A more valuable than B. The study demonstrated MCDA's potential to structure complex decisions around orphan drugs in a transparent, replicable manner. Further research is needed to validate attributes and determine a consensus weighting approach for potential cross-country orphan drug valuation.
Technology Assessment, Outcomes Research and Economic Analysesevadew1
This document discusses technology assessment, outcomes research, and economic analyses in healthcare. It provides background on rising healthcare costs in the US and outlines a hierarchy for assessing new medical technologies from technical efficacy to patient and societal outcomes. Randomized controlled trials are described as the gold standard but limitations are noted. Alternative study designs like modeling and assessing intermediate outcomes are proposed when RCTs are not feasible. The document uses CT for appendicitis as an example to work through initial steps in outcomes research. It also discusses limitations and alternative outcomes like assessing the therapeutic value of diagnostic tests.
The document discusses the EQ-5D, a standardized instrument used to measure health outcomes. It describes the EQ-5D as comprising a descriptive system covering 5 dimensions of health and a visual analog scale (EQ-VAS). Country-specific value sets allow EQ-5D health states to be converted into a single summary index number representing health-related quality of life. The EQ-5D is widely used internationally in cost-effectiveness analysis and other areas to inform healthcare decisions. Challenges in analyzing EQ-5D data and future directions for outcomes measurement are also addressed.
Multi criteria decision analysis for healthcareSunhong Kwon
Multi-criteria decision analysis (MCDA) is a process used to evaluate multiple criteria in healthcare decision making. Three key aspects of MCDA are summarized:
1. MCDA identifies criteria like clinical benefits, safety, costs, and other factors to evaluate healthcare interventions. Studies consider criteria like disease severity, population affected, clinical guidelines, and costs.
2. Interventions are measured and scored on each criterion using evidence and expert opinion. Scores are often on predefined scales.
3. Criteria weights reflecting importance are generated, commonly using analytic hierarchy process or defined scales. Weights involve multiple stakeholders.
Sensitivity analysis assesses uncertainty, though many studies do not report this. MCD
The document discusses the formation of an ISPOR task force to develop guidance on the use of multi-criteria decision analysis (MCDA) in healthcare decision making. The task force will define MCDA, identify different MCDA techniques, and provide guidance on which techniques are best suited for different types of healthcare decisions. The document also discusses proposed definitions of MCDA and debates the appropriate scope and focus of the task force's work.
The document summarizes Martina Garau's presentation on assessing the value of co-dependent technologies such as diagnostic tests and treatments. It discusses how co-dependent technologies can create additional value dimensions beyond traditional cost-effectiveness analysis, challenges to valuing and proving the benefits of these technologies, and examples from countries like Australia and the UK. The presentation analyzes frameworks for capturing different value elements, barriers to evidence generation, and proposes processes for jointly assessing diagnostics and treatments.
Technologies that enhance the precision and effect of therapies can make a critical contribution to ensuring value for money and improving patient care. Methods and processes for assessing value, however, still are imperfect. This presentation reviews the challenges and identifies some approaches for meeting them.
Adam Steventon: How can predictive risk models help?Nuffield Trust
This document discusses how predictive risk models can help evaluate interventions in observational studies. It notes challenges with observational studies, such as regression to the mean and lack of control groups. It proposes several solutions to address these challenges, including before-after studies, regression adjustment, matching controls, and regression discontinuity designs. Matched controls are described as a way to reduce dependence on regression model specification by "data pre-processing" to compare intervention patients to similar control patients. The document concludes by surveying the current state of telehealth studies, finding most are descriptive or use before-after designs, with few employing more rigorous controlled designs.
Martina Garau presented on using multi-criteria decision analysis (MCDA) to value orphan medicines. A pilot study developed a value framework with 10 attributes related to disease impact and treatment benefits. Expert and patient workshops then weighted the attributes and applied them to two case studies, finding treatment A more valuable than B. The study demonstrated MCDA's potential to structure complex decisions around orphan drugs in a transparent, replicable manner. Further research is needed to validate attributes and determine a consensus weighting approach for potential cross-country orphan drug valuation.
Technology Assessment, Outcomes Research and Economic Analysesevadew1
This document discusses technology assessment, outcomes research, and economic analyses in healthcare. It provides background on rising healthcare costs in the US and outlines a hierarchy for assessing new medical technologies from technical efficacy to patient and societal outcomes. Randomized controlled trials are described as the gold standard but limitations are noted. Alternative study designs like modeling and assessing intermediate outcomes are proposed when RCTs are not feasible. The document uses CT for appendicitis as an example to work through initial steps in outcomes research. It also discusses limitations and alternative outcomes like assessing the therapeutic value of diagnostic tests.
The document discusses the EQ-5D, a standardized instrument used to measure health outcomes. It describes the EQ-5D as comprising a descriptive system covering 5 dimensions of health and a visual analog scale (EQ-VAS). Country-specific value sets allow EQ-5D health states to be converted into a single summary index number representing health-related quality of life. The EQ-5D is widely used internationally in cost-effectiveness analysis and other areas to inform healthcare decisions. Challenges in analyzing EQ-5D data and future directions for outcomes measurement are also addressed.
Adjusting for Differential Item Functioning in the EQ-5D-5L Using Externally-...Office of Health Economics
Paula and Rachel's presentation on adjusting for differential item functioning in the EQ-5Q-5L using externally-collected vignettes given at the 2017 iHEA World Conference in Boston.
The document summarizes background information on the UK's National Institute for Health and Care Excellence (NICE) taking into account additional factors like burden of illness and wider societal benefits in its appraisal of new health technologies. It discusses NICE receiving over 900 comments on its proposals to incorporate these new factors and its recommendation against changes in the short term. The rest of the document outlines the agenda and issues to be discussed in the workshop, including evidence from stated preference studies on societal values regarding severity of illness, end of life treatments, and proportional shortfall.
This document discusses technology assessment, outcomes research, and economic analyses in healthcare. It provides background on rising healthcare costs in the US without clear improvements in health outcomes compared to other countries. The rationale for assessing new technologies and their impact is described. Key aspects of technology assessment are outlined, including technical efficacy, diagnostic accuracy, diagnostic impact, therapeutic impact, patient outcomes, and societal outcomes. Challenges with randomized controlled trials in assessing technologies are reviewed. The National Lung Screening Trial is presented as an example. Finally, computed tomography for appendicitis is analyzed as a hypothetical example of how modeling could be used to assess a technology when a randomized trial may not be feasible.
Implementing psychosocial care into routine practice: making it easyCancer Institute NSW
1. This document discusses implementing a clinical pathway for screening and managing anxiety and depression in cancer patients. It outlines barriers to implementation and strategies to address them.
2. A key barrier is that screening alone does not improve outcomes; a clear clinical pathway and institutional support are needed. The pathway was developed through stakeholder consultation and specifies screening, assessment, referral, and treatment steps.
3. Barriers to implementing the pathway include lack of resources, responsibility issues, staff and patient reluctance. The proposed study will test intensive versus basic strategies to promote pathway uptake, including online training, automated screening/referral systems, and patient/staff educational resources. The goal is to improve psychosocial outcomes for cancer patients.
The document discusses early assessment of whole exome sequencing (WES) as a potential genetic test for children with complex neurological disorders. It presents 5 scenarios for using WES in the current diagnostic trajectory. Analysis shows all scenarios except using WES only as an add-on test reduce costs compared to current practice. Early health technology assessment can explore how innovations may enhance healthcare and guide implementation, but uncertainties remain regarding technology development, interpretation, and downstream costs.
Innovative Strategies For Successful Trial Design - Webinar SlidesnQuery
Full webinar available here: https://www.statsols.com/webinar/innovative-strategies-for-successful-trial-design
[Webinar] Innovative Strategies For Successful Trial Design- In this free webinar, you will learn about:
- The challenges facing your trials
- How to calculate the correct sample size
- Worked examples including Mixed/Hierarchical Models
- Posterior Error
- Adaptive Designs For Survival
www.statsols.com
Achieving behaviour change for patient safety, Judith Dyson, Lecturer, Mental Health - University of Hull
Presentation from the Patient Safety Collaborative launch event held in London on 14 October 2014
More information at http://www.nhsiq.nhs.uk/improvement-programmes/patient-safety/patient-safety-collaboratives.aspx
This document summarizes discussions around improving early patient access to innovative new drugs in Europe. It discusses the perspectives and needs of various stakeholders in health care systems, including patients, regulators, payers, providers, and drug companies. It also evaluates different approaches that have been tried or proposed, such as adaptive licensing pathways, managed entry agreements between drug companies and health systems, and performance-based risk-sharing arrangements. Finally, it outlines areas where more progress could still be made, such as increased coordination between regulatory agencies and health technology assessment bodies, better use of disease registries and electronic health records to generate real-world evidence, and public-private partnerships to support these efforts.
This document outlines how the National Institute for Health and Care Excellence (NICE) uses cost-effectiveness analysis to inform reimbursement decisions in the UK. It discusses NICE's process and how it generally accepts interventions with an incremental cost-effectiveness ratio of less than £20,000-30,000 per quality-adjusted life year (QALY). The document emphasizes the important role of the EQ-5D questionnaire in NICE's decisions by allowing comparison of health outcomes. It addresses issues like collecting EQ-5D data, mapping from other measures, and potential limitations of EQ-5D for certain conditions.
'Demystifying Knowledge Transfer- an introduction to Implementation Science M...NEQOS
Powerpoint presentation from 'Demystifying Knowledge Transfer: an introduction to Implementation Science' - 28th May 2014.
Facilitated by Professor Jeremy Grimshaw and Dr Justin Presseau
This document discusses frameworks for assessing the value of co-dependent technologies, such as diagnostic and treatment pairs. It defines co-dependent technologies as those used together to achieve clinical benefits. The value created by co-dependent technologies is difficult to attribute to the diagnostic or treatment alone. The document outlines five ways that diagnostic-treatment pairs can create value: by reducing adverse effects, reducing time delays in optimal treatment selection, increasing adherence to treatment, enabling treatments for small subsets of patients, and reducing uncertainty about treatment value. It also discusses factors that affect the value of co-dependent technologies and international experiences with health technology assessment of these pairs.
Using Implementation Science to transform patient care (Knowledge to Action C...NEQOS
Master Class presentation and workshop materials from the NENC AHSN Collaborating for Better Care Partnership's Master Class, led by Professor Jeremy Grimshaw' on 1st September 2014
What does the public think about assigning priority to end-of-life treatment? In this presentation, OHE's Koonal Shah describes the results of research intended to tease out both preferences and, where possible, the reasoning behind them. The findings may surprise some -- for example, that priority is not given to end-of-life treatments when the treatments they would supplant offer greater health gains.
Non-inferiority and Equivalence Study design considerations and sample sizenQuery
About the webinar
This webinar examines the role of non-inferiority and equivalence in study design
In this free webinar, you will learn about:
-Regulatory information on this type of study design
-Considerations for study design and your sample size
-Practical worked examples of
--Non-inferiority Testing
--Equivalence Testing
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Watch the video at: https://www.statsols.com/webinars
Evaluating the cost effectiveness of diagnostic tests ScHARR HEDS
Evaluating the cost-effectiveness of diagnostic tests requires modeling their accuracy, the downstream patient experiences and costs based on test outcomes, and calculating the incremental cost-effectiveness ratio compared to current practice. For new diagnostic tests for sepsis, models would estimate quality of life and costs for high, medium, and low risk groups under different testing strategies incorporating a risk assessment tool and more accurate but costly test. Additional complications in evaluating diagnostics include understanding complex patient pathways, limitations of available data, dependencies between tests, and imperfect reference standards.
Operational research is becoming important in real world setting of health care as it always tried to find out challenges or gaps in any health related issues or in program. For health program improvement, OR should be conducting frequently. Program manager and doctors should be involve in OR and encourage to do so.
Does measurement lead to better health outcomes 20161202Tienie Stander
Health outcomes research, real world evidence, registries, comparative effectiveness trials: all are current buzzwords. However, the ultimate question is whether all these measurements lead to better health outcomes? We explore this question from a philosophical and practical perspective
Academic Report on Japan Clinical Trial Strategy for a Class III Medical DeviceAsia Medical Supplies
The document summarizes the clinical trial strategy for Axcis Plus, a Class III medical device developed by Japan Med to treat refractory angina. Japan Med plans to conduct a phase 3 randomized controlled trial in Japan to evaluate the device's safety and efficacy. The trial will enroll 84 patients and compare outcomes between an intervention group receiving Axcis Plus treatment and a control group. Primary endpoints will assess safety, while secondary endpoints will evaluate effectiveness and secondary assessments. Challenges include physician technique dependence and ensuring accurate data collection. The trial must comply with Japanese good clinical practice regulations and be approved by regulatory agencies.
Strategies for Analgesic Development and the FDA Guidance for Analgesic Indic...Brook White, PMP
This presentation provides a high-level review of FDA’s guidance, offers strategies for analgesic product development and shares experiences with PROs and paper vs. tablet collection.
ICN Victoria presents Dr Dashiell Gantner, research fellow at the Monash University in Melbourne. Here he talks about translating ICU research into clinical practice.
> Why HEOR?
> Costs, Consequences and Perspectives
> Key Stakeholders in HEOR
> What is Health Economics and Pharmaco-economic Research?
> Economic Evaluations
> Incremental Cost Effectiveness Ratio (ICER)
> Concept of HRQoL
> Comparative Effectiveness Research (CER)
> Pragmatic Clinical Trials
> Observational Studies
> Systematic Reviews and Meta-Analysis
> Application of CER
> Health Technology Assessment (HTA)
> Real World Evidence (RWE)
> Patient Reported Outcomes (PROs)
> Patient Focused Drug Development (PFDD)
> Application of Health Economic Evaluations
> Challenges and Barriers
Adjusting for Differential Item Functioning in the EQ-5D-5L Using Externally-...Office of Health Economics
Paula and Rachel's presentation on adjusting for differential item functioning in the EQ-5Q-5L using externally-collected vignettes given at the 2017 iHEA World Conference in Boston.
The document summarizes background information on the UK's National Institute for Health and Care Excellence (NICE) taking into account additional factors like burden of illness and wider societal benefits in its appraisal of new health technologies. It discusses NICE receiving over 900 comments on its proposals to incorporate these new factors and its recommendation against changes in the short term. The rest of the document outlines the agenda and issues to be discussed in the workshop, including evidence from stated preference studies on societal values regarding severity of illness, end of life treatments, and proportional shortfall.
This document discusses technology assessment, outcomes research, and economic analyses in healthcare. It provides background on rising healthcare costs in the US without clear improvements in health outcomes compared to other countries. The rationale for assessing new technologies and their impact is described. Key aspects of technology assessment are outlined, including technical efficacy, diagnostic accuracy, diagnostic impact, therapeutic impact, patient outcomes, and societal outcomes. Challenges with randomized controlled trials in assessing technologies are reviewed. The National Lung Screening Trial is presented as an example. Finally, computed tomography for appendicitis is analyzed as a hypothetical example of how modeling could be used to assess a technology when a randomized trial may not be feasible.
Implementing psychosocial care into routine practice: making it easyCancer Institute NSW
1. This document discusses implementing a clinical pathway for screening and managing anxiety and depression in cancer patients. It outlines barriers to implementation and strategies to address them.
2. A key barrier is that screening alone does not improve outcomes; a clear clinical pathway and institutional support are needed. The pathway was developed through stakeholder consultation and specifies screening, assessment, referral, and treatment steps.
3. Barriers to implementing the pathway include lack of resources, responsibility issues, staff and patient reluctance. The proposed study will test intensive versus basic strategies to promote pathway uptake, including online training, automated screening/referral systems, and patient/staff educational resources. The goal is to improve psychosocial outcomes for cancer patients.
The document discusses early assessment of whole exome sequencing (WES) as a potential genetic test for children with complex neurological disorders. It presents 5 scenarios for using WES in the current diagnostic trajectory. Analysis shows all scenarios except using WES only as an add-on test reduce costs compared to current practice. Early health technology assessment can explore how innovations may enhance healthcare and guide implementation, but uncertainties remain regarding technology development, interpretation, and downstream costs.
Innovative Strategies For Successful Trial Design - Webinar SlidesnQuery
Full webinar available here: https://www.statsols.com/webinar/innovative-strategies-for-successful-trial-design
[Webinar] Innovative Strategies For Successful Trial Design- In this free webinar, you will learn about:
- The challenges facing your trials
- How to calculate the correct sample size
- Worked examples including Mixed/Hierarchical Models
- Posterior Error
- Adaptive Designs For Survival
www.statsols.com
Achieving behaviour change for patient safety, Judith Dyson, Lecturer, Mental Health - University of Hull
Presentation from the Patient Safety Collaborative launch event held in London on 14 October 2014
More information at http://www.nhsiq.nhs.uk/improvement-programmes/patient-safety/patient-safety-collaboratives.aspx
This document summarizes discussions around improving early patient access to innovative new drugs in Europe. It discusses the perspectives and needs of various stakeholders in health care systems, including patients, regulators, payers, providers, and drug companies. It also evaluates different approaches that have been tried or proposed, such as adaptive licensing pathways, managed entry agreements between drug companies and health systems, and performance-based risk-sharing arrangements. Finally, it outlines areas where more progress could still be made, such as increased coordination between regulatory agencies and health technology assessment bodies, better use of disease registries and electronic health records to generate real-world evidence, and public-private partnerships to support these efforts.
This document outlines how the National Institute for Health and Care Excellence (NICE) uses cost-effectiveness analysis to inform reimbursement decisions in the UK. It discusses NICE's process and how it generally accepts interventions with an incremental cost-effectiveness ratio of less than £20,000-30,000 per quality-adjusted life year (QALY). The document emphasizes the important role of the EQ-5D questionnaire in NICE's decisions by allowing comparison of health outcomes. It addresses issues like collecting EQ-5D data, mapping from other measures, and potential limitations of EQ-5D for certain conditions.
'Demystifying Knowledge Transfer- an introduction to Implementation Science M...NEQOS
Powerpoint presentation from 'Demystifying Knowledge Transfer: an introduction to Implementation Science' - 28th May 2014.
Facilitated by Professor Jeremy Grimshaw and Dr Justin Presseau
This document discusses frameworks for assessing the value of co-dependent technologies, such as diagnostic and treatment pairs. It defines co-dependent technologies as those used together to achieve clinical benefits. The value created by co-dependent technologies is difficult to attribute to the diagnostic or treatment alone. The document outlines five ways that diagnostic-treatment pairs can create value: by reducing adverse effects, reducing time delays in optimal treatment selection, increasing adherence to treatment, enabling treatments for small subsets of patients, and reducing uncertainty about treatment value. It also discusses factors that affect the value of co-dependent technologies and international experiences with health technology assessment of these pairs.
Using Implementation Science to transform patient care (Knowledge to Action C...NEQOS
Master Class presentation and workshop materials from the NENC AHSN Collaborating for Better Care Partnership's Master Class, led by Professor Jeremy Grimshaw' on 1st September 2014
What does the public think about assigning priority to end-of-life treatment? In this presentation, OHE's Koonal Shah describes the results of research intended to tease out both preferences and, where possible, the reasoning behind them. The findings may surprise some -- for example, that priority is not given to end-of-life treatments when the treatments they would supplant offer greater health gains.
Non-inferiority and Equivalence Study design considerations and sample sizenQuery
About the webinar
This webinar examines the role of non-inferiority and equivalence in study design
In this free webinar, you will learn about:
-Regulatory information on this type of study design
-Considerations for study design and your sample size
-Practical worked examples of
--Non-inferiority Testing
--Equivalence Testing
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Watch the video at: https://www.statsols.com/webinars
Evaluating the cost effectiveness of diagnostic tests ScHARR HEDS
Evaluating the cost-effectiveness of diagnostic tests requires modeling their accuracy, the downstream patient experiences and costs based on test outcomes, and calculating the incremental cost-effectiveness ratio compared to current practice. For new diagnostic tests for sepsis, models would estimate quality of life and costs for high, medium, and low risk groups under different testing strategies incorporating a risk assessment tool and more accurate but costly test. Additional complications in evaluating diagnostics include understanding complex patient pathways, limitations of available data, dependencies between tests, and imperfect reference standards.
Operational research is becoming important in real world setting of health care as it always tried to find out challenges or gaps in any health related issues or in program. For health program improvement, OR should be conducting frequently. Program manager and doctors should be involve in OR and encourage to do so.
Does measurement lead to better health outcomes 20161202Tienie Stander
Health outcomes research, real world evidence, registries, comparative effectiveness trials: all are current buzzwords. However, the ultimate question is whether all these measurements lead to better health outcomes? We explore this question from a philosophical and practical perspective
Academic Report on Japan Clinical Trial Strategy for a Class III Medical DeviceAsia Medical Supplies
The document summarizes the clinical trial strategy for Axcis Plus, a Class III medical device developed by Japan Med to treat refractory angina. Japan Med plans to conduct a phase 3 randomized controlled trial in Japan to evaluate the device's safety and efficacy. The trial will enroll 84 patients and compare outcomes between an intervention group receiving Axcis Plus treatment and a control group. Primary endpoints will assess safety, while secondary endpoints will evaluate effectiveness and secondary assessments. Challenges include physician technique dependence and ensuring accurate data collection. The trial must comply with Japanese good clinical practice regulations and be approved by regulatory agencies.
Strategies for Analgesic Development and the FDA Guidance for Analgesic Indic...Brook White, PMP
This presentation provides a high-level review of FDA’s guidance, offers strategies for analgesic product development and shares experiences with PROs and paper vs. tablet collection.
ICN Victoria presents Dr Dashiell Gantner, research fellow at the Monash University in Melbourne. Here he talks about translating ICU research into clinical practice.
> Why HEOR?
> Costs, Consequences and Perspectives
> Key Stakeholders in HEOR
> What is Health Economics and Pharmaco-economic Research?
> Economic Evaluations
> Incremental Cost Effectiveness Ratio (ICER)
> Concept of HRQoL
> Comparative Effectiveness Research (CER)
> Pragmatic Clinical Trials
> Observational Studies
> Systematic Reviews and Meta-Analysis
> Application of CER
> Health Technology Assessment (HTA)
> Real World Evidence (RWE)
> Patient Reported Outcomes (PROs)
> Patient Focused Drug Development (PFDD)
> Application of Health Economic Evaluations
> Challenges and Barriers
The document summarizes a literature review examining decision-making methodologies used in health technology appraisal processes. It describes common decision criteria identified in the literature, including efficacy, safety, disease burden, quality of evidence, ethics and cost-effectiveness. Decision-making methods reviewed integrate these criteria through approaches like assigning weights, ranking criteria or using decision rules. The literature search included publications from organizations in several countries.
Medical and scientific advances and ethicskeshavpodanobp
Medical science is the branch of science focused on understanding how the human body works, preventing and diagnosing diseases, and finding ways to treat illnesses and injuries. It involves studying the body's systems, cells, and molecules to develop treatments that can help people live healthier lives. Medical advancements include things like new medicines, vaccines, surgical techniques, and diagnostic tools that help doctors provide better care for their patients. Overall, medical science aims to improve people's health and well-being by finding ways to prevent, diagnose, and treat diseases and injuries.
This document discusses economic evaluation and how to critically appraise economic evaluation studies. It begins by outlining the purpose of economic evaluation and different forms of evaluation like cost-minimization analysis, cost-effectiveness analysis, and cost-utility analysis. Guidelines for critical appraisal are presented, including questions about the study question, alternatives compared, effectiveness and costs measured, and handling of costs and consequences. While guidelines aim to improve quality, there is disagreement around issues like perspective, comparators, discount rates, and equity. Overall, economic evaluation can inform decision-making, but critical appraisal of studies requires addressing areas of both agreement and ongoing debate.
The document discusses the need for more consistency in outcomes reported across clinical trials. It introduces the Core Outcome Measures in Effectiveness Trials (COMET) Initiative, which aims to develop standardized "core outcome sets" that define the minimum outcomes that should be reported in all trials for specific clinical areas. The COMET Initiative website provides resources for developing outcome sets and identifying existing related work to avoid duplication. Stakeholders like funders and journal editors are encouraged to support the use of core outcomes in order to make trial results more useful for patients and healthcare decision-making.
Health Technology Assessment: Comparison between UK and Canada Processes by D...Dr. Tayaba Khan
The document compares the health technology assessment (HTA) processes in the UK and Canada. Both countries have national HTA agencies that evaluate new health technologies. The UK's agency is NICE, while Canada's is CADTH. They share principles like considering clinical efficacy, cost-effectiveness, and patient input. However, their processes differ in that NICE recommendations must be followed, while CADTH's are non-binding. Overall, the UK and Canada demonstrate similarities in HTA aims but differences in implementation and impact due to unique healthcare systems and policy environments.
This document discusses maximizing patient outcomes in respiratory care. It outlines the founding principles of the Respiratory Effectiveness Group (REG), which aims to better integrate real-world evidence from sources like observational studies and pragmatic trials into clinical practice guidelines. Currently, guidelines are often based primarily on randomized controlled trials, which have limitations and may not generalize to most patients. The document calls for considering a diversity of evidence and tailoring care to individual patient needs and characteristics. It also discusses how databases could help achieve more personalized care by providing real-world data on topics like disease prevalence, treatments, and outcomes across different healthcare systems.
This document summarizes current recommendations and gaps regarding extrapolation of time-to-event outcomes from clinical trials. It reviewed 11 methodological papers and 5 guidelines on extrapolating survival data. The guidelines, particularly from NICE, provide a detailed process for extrapolation including testing different survival models, validating the best fitting model, and using external data for validation. However, the guidelines need updating to apply to more disease areas beyond oncology and different time-to-event outcomes.
This timely presentation addresses the changes that are proposed under NICE's new value-based assessment (VBA) approach to assessing health technologies. It reviews NICE's current approach and decisions to date for all technologies and separately for orphan and cancer drugs. VBA's proposed calculations for burden of illness and societal impact use estimates of 'shortfall' are illustrated in the presentation. Also discussed are changes in QALY thresholds.
How to Define Effective and Efficient Real World TrialsTodd Berner MD
This document discusses strategies for designing effective and efficient real-world clinical trials. It covers topics such as using real-world evidence to inform clinical trial design, the differences between efficacy and effectiveness, challenges around representativeness in trial populations, and the value of pragmatic clinical trials. It also discusses leveraging electronic health records for condition-specific prompts and clinical decision support to improve performance and quality of care.
How to design effective and efficient real world trials TB Evidence 2014 10.2...Todd Berner MD
This document discusses strategies for designing effective and efficient real-world clinical trials. It covers topics such as using real-world evidence to inform clinical trial design, the differences between efficacy and effectiveness, challenges in defining quality metrics, and strategies for improving performance within healthcare systems. The document provides information on pragmatic clinical trials and how real-world evidence could reduce costs compared to traditional clinical trials.
Evaluating the impact of HTA and ‘better decision-making’ on health outcomescheweb1
This document outlines a conceptual framework for assessing the impact of health technology assessments (HTA). It begins by discussing what is already known about evaluating HTA, including the limited literature on long-term effects and barriers to implementation. The document then presents two case studies and proposes a theory-driven, realist approach to impact assessment using configurations of context, mechanism, and outcomes. Interviews and primary data collection are suggested to test an initial program theory regarding how and why HTA influences policy and practice. The goal is to produce guidance on effective implementation by understanding what works, for whom, and in what contexts.
Structured decision making approaches to the inclusion of multiple criteria i...Office of Health Economics
This document discusses the use of multi-criteria decision analysis (MCDA) approaches in health technology assessment (HTA). It notes that HTA already considers multiple criteria beyond just health impacts and costs. The document reviews pilots of MCDA in HTA globally and identifies challenges to implementing MCDA, such as how to establish and weight criteria. It concludes that MCDA can provide a coherent framework for HTA if methodological issues are addressed, but that approaches need to be tailored to different healthcare systems.
This document discusses cost-effectiveness analysis (CEA) and calculating the incremental cost-effectiveness ratio (ICER). There are 4 main types of CEA: cost-minimization analysis, CEA using natural units, cost-utility analysis using quality-adjusted life years (QALYs), and cost-benefit analysis using monetary units. The ICER is calculated as the incremental cost divided by the incremental effectiveness (e.g. cost per QALY gained) of a new intervention compared to the existing one. Key information needed includes outcomes and costs of both the existing and new interventions. An example ICER calculation for a new treatment for thingyitis is provided. Thresholds of willingness to pay per QAL
1. The document discusses pharmacoeconomics in India, including guidelines for conducting pharmacoeconomic evaluations. Some key guidelines covered include identifying relevant perspectives and comparators, choosing appropriate analytical techniques, establishing appropriate time horizons, and properly accounting for costs and effects.
2. Case studies on the cost-effectiveness of treating osteoarthritis pain and deep vein thrombosis are provided. For osteoarthritis, NSAIDs may be more cost-effective than COX-II inhibitors due to lower costs and risks, depending on risk of GI issues. For deep vein thrombosis, low molecular weight heparin enables outpatient treatment due to lower monitoring needs despite higher costs.
3. Pharmacoeconomics can help inform healthcare decisions
This document discusses guidelines for evidence-based medicine. It describes the importance of using high-quality research evidence and expertise to inform clinical guidelines. Guidelines are developed through a rigorous process that involves forming a multidisciplinary group, conducting systematic reviews of the literature, critically appraising evidence, and linking recommendations directly to evidence through a grading system. The goals are to promote best practices, reduce unwarranted variation, and improve patient outcomes and health care delivery.
KT research involves studying how to effectively promote the uptake of knowledge into clinical practice. Passive educational activities like conferences are generally ineffective at changing physician behavior, while knowledge translation approaches in the clinical environment using tools like clinical pathways and decision support can impact outcomes. The examples described implemented guidelines for diagnosing pulmonary embolism and increased physician use of electronic resources through a mobile clinical decision support system.
A well recognised form of research is called systematic reviews on specific point. Why do we need them and How they can be done?? this talk is trying to answer these questions in a simple way
Similar to Factors Influencing Decisions by NICE (20)
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Factors Influencing Decisions by NICE
1. The Influence of Cost-Effectiveness and
Other Factors on NICE Decisions
OHE Lunchtime Seminar
London • 23 April 2013
2. The influence of cost-effectiveness
and other factors on NICE decisions
Helen Dakin1 and Nancy Devlin2
in collaboration with
Yan Feng2, Nigel Rice3, Phill O’Neill2 and David Parkin2
1University of Oxford, 2OHE, 3Unversity of York
4. NICE
Established 1999
Issues guidance to 'ensure quality and value for money'
For technology appraisals 'NHS is required to provide
funding and resources for medicines and treatments
recommended by NICE'
Important implications for patients, the NHS, industry,
decision making in other countries
What factors affect NICE decisions? How important is
cost effectiveness compared to ‘other factors’?
5. NICE’s stated threshold
Pre-2004: various statements suggest a threshold of around
£30,000
2004 methods guide & 2005 social value judgements
• '[NICE] should, generally, accept as cost effective those interventions
with an incremental cost-effectiveness ratio of less than £20,000 per
QALY and that there should be increasingly strong reasons for
accepting as cost effective interventions with an incremental cost-
effectiveness ratio of over £30,000 per QALY
2008 social value judgements
• NICE should explain its reasons when it decides that an intervention
with an ICER below £20,000 per QALY gained is not cost effective;
and when an intervention with an ICER of more than £20,000 to
£30,000 per QALY gained is cost effective
6. Previous studies on
determinants of NICE decisions
Study No.
Appraisals
Model Findings
Devlin &
Parkin (2004)
39 to May
2002
Logistic:
yes/no
• Threshold ≈£40,000/QALY
• Uncertainty & prevalence matter
Dakin et al
(2006)
73 to Dec
2003
mlogit: yes /
no / yes, but
• ICER, number trials or SRs, date, patient group
submissions and technology type matter
• ‘Yes, but’ and ‘no’ driven by different factors
Jena (2009) 86 to 2005 Linear
probability
model:
yes/no
• £1000 increase in ICER decreases probability
of ‘yes’ by 0.009
• Infectious disease and mental health decisions
more likely to be ‘no’
Mason &
Drummond
(2009)
38 cancer
appraisals
Oct 2008
Tabulation:
yes / no /
yes, but
• ‘No’ more likely after 2006: partly due to STA
process?
• Restrictions attributed to ICER, insufficient
evidence, uncertainty or methodology
7. Aims
Estimate NICE’s cost-effectiveness threshold
Identify the factors that affect or explain NICE’s decisions
Evaluate whether NICE’s threshold or decision-making has changed
over time [in progress]
9. The basic model
Incremental cost-effectiveness ratio (ICER)
• Hypothesised to be main driver of decisions
• ICER in £000s
Clinical evidence
• ‘NICE should not recommend an intervention […] if there is […] not
enough evidence’ (SVJ 2008)
• Hypothesis: NICE will reject if insufficient evidence
• Total number of patients in randomised trials = number RCTs x mean
patients/trial
Insights provided by stakeholders (Rawlins 2010)
• E.g. on whether QoL assessment adequately captures benefits (SVJ 2008)
• Hypothesis: increases odds of ‘yes’
• Patient group submission =1 if patient group submitted evidence or
opinion (proxy for stakeholder involvement/persuasion)
10. The basic model (cont.)
Only treatment
• Hypothesis: NICE is more likely to recommend if no alternatives
• =1 if there were no alternative treatments available for this patient group
Children
• Give ‘the benefit of the doubt’ given methodological challenges (Rawlins 2010)
• Hypothesis: paediatric treatments more likely to be recommended
• =1 if the decision specifically concerns children or adolescents
Publication date
• Evaluates whether NICE decisions are changing
• No prior hypothesis
• =Years since first NICE appraisal was published
Severity of underlying illness
• NICE states that is accept higher ICERs for serious conditions (Rawlins 2010)
• = Mean WHO DALY weight across conditions for this disease category
11. Additional variables explored
Pharmaceutical
• May reflect greater stakeholder involvement
• =1 for all drugs
Disease
• Interim analysis suggests NICE gives extra weight to cancer treatments
• 8 dummies =1 if the decision concerns that disease
• Diseases with <20 decisions with ICERs omitted
Probabilistic sensitivity analysis (PSA)
• Significant predictor of AWMSG decisions (Linley & Hughes 2012)
• =1 if the model has PSA
Broader perspective
• Reflects consideration of additional savings not captured in ICER
• =1 if non-NHS/PSS costs were analysed or discussed
12. Additional variables explored (cont.)
Appraisal committee
• Because committees weigh both quantitative evidence and value
judgements, the way they do this might differ systematically across
committees.
• Committees characterised by their Chairs and included as dummies
Innovation
• NICE says that it takes into account the innovative nature of the
technology
• Defined by us as: any molecule launched within 2 years of appraisal
AND in an ATC4 class that was created 5 years prior to appraisal.
This picks up new medicines, but also avoids limiting to first in class as
NICE does assess groups of similar medicines that are new and would
capture the spirit of viewing a medicines as innovative – e.g. new
diabetes medicines, TNFs, etc.
13. Additional variables explored (cont.)
Single technology appraisal (STA)
• Mason & Drummond found cancer STAs more likely to be ‘no’
• =1 if the STA process was used
Orphan
• Evaluate […] ‘orphan drugs’, in the same way as any other treatment
(SVJ, 2008; Littlejohns and Rawlins, 2009)
• Hypothesised to have no impact based on NICE statements
• =1 if the treatment has EMEA orphan status
Uncertainty
• Difference between the highest and lowest NE quadrant ICERs
• 2 dummies indicating whether the plausible ICER could be dominant,
or be dominated were explored, but dropped out of regression
• Other measures of uncertainty are problematic
14. Additional variables explored
on a subset of decisions
End of life
• Place special value on treatments prolonging life at the end of life,
providing that life is of reasonable quality (Rawlins, 2010; NICE 2009)
• =1 if met EoL criteria
• Only evaluated for decisions with preliminary guidance (FAD)
published after 5th Jan 2009
16. HTAinSite
Most data derived from HTAinSite: www.htainsite.com
Commercial database developed by OHE, Abacus and City
University
Provides extensive data on
all NICE and SMC appraisals
• Regularly updated
• Extracted and validated based
on established protocol
Access to web interface
available to subscribers
Academics can request
data for research by email
17. Appraisals versus decisions
We analyse binary yes/no choices (not yes/no/yes, but)
• Evidence, ICERs and other considerations often differ by subgroup for
which the technology is recommended and those for which it is
rejected
• Levels of restrictions differ enormously from 5% of patients to 80%
(O’Neill and Devlin 2010)
• Reflects HTAinSite protocol
NICE appraisals are divided into yes/no decisions concerning
whether or not to use one technology in one patient
subgroup with a certain condition
• Methods for subdividing appraisals governed by HTAinSite protocol
18. Collection of ICER data
Most guidance documents give multiple ICERs
• From manufacturer, assessment group and decision support unit
• Base case, subgroup analyses and sensitivity analyses
• Several comparators
HTAinSite records all ICERs mentioned in documentation
We developed a protocol to identify the ICERs informing each
decision
• Included only cost/QALY ICERs for the subgroup(s) in that decision
• DSU or ERG/TAG ICERs used in preference to manufacturer
• Vs NICE’s preferred comparator or next most effective treatment on the
frontier
• Exclude ICERs that NICE did not believe (based on considerations section)
19. Decisions with multiple ICERs
A decision may have >1 relevant ICER if
• It covers >1 subgroup with different ICERs
• Results of several analyses or comparators are given equal prominence
in guidance document
• NICE concluded the ICER was ‘between X and Y’, <A or >B
Taking the mean or midpoint would ignore uncertainty & make
assumptions about how NICE uses ICER data
We therefore randomly sampled from the list of ICERs
• Each ICER was given equal weight
• For ranges, we sampled from the full list of ICERs from other decisions
• Drew 100 iterations, each with different ICERs for each decision
• Analyses repeated on each iteration; results combined with Rubin’s
Rules
21. Outline of econometric methods
Used logistic regression to predict the effect of ICER and
other variables on the log-odds of NICE saying ‘yes’
Adjusted standard errors to allow for clustering of decisions
within appraisals
Analysed in Stata version 12
22. Modelling strategy
Stage 0: Estimation of ICER-only model where ICER alone
predicts recommendations
Stage 1: Evaluate a 'basic model' with the variables expected to
have most impact on NICE decisions
Stage 2: Remove non-significant variables from basic model one
at a time
Stage 3: Evaluate impact of adding additional variables
Stage 4: Alternative specifications of basic model parameters [to
come]
Stage 5: Sensitivity and subgroup analyses [to come]
23. Model selection
Our methods for dealing with decisions with ≥2 ICERs
involve generating 100 datasets with different ICER values
We combine coefficients across datasets using Rubin’s Rules
AIC and pseudo-R2 cannot be pooled across datasets
We therefore choose between models based on prediction
accuracy
• We assume that model predicts a ‘yes’ if predicted log-odds ≥0
• Categorise decisions into true/false positives and true/false negatives
to get the % of decisions correctly predicted
24. Additional variables explored
on a subset of decisions
End of life
• Place special value on treatments prolonging life at the end of life,
providing that life is of reasonable quality (Rawlins 2010; NICE 2009)
• =1 if met EoL criteria
• Only evaluated for decisions with preliminary guidance (FAD)
published after 5th Jan 2009
26. Numbers of decisions and appraisals
240 appraisals
published by 31st
December 2011 E1 & E2: 11 terminated
appraisals & 12 decisions
without other restrictions
excluded
229 appraisals
comprising 763
decisions
E3a: 162 decisions based on
grounds other than cost-
effectiveness
E3b-c: 92 decisions based on
cost-effectiveness but without
available, quantified cost/QALY
510 decisions included
in models with ICERs
28. Number of ICERs
510 decisions have available quantified cost/QALY ICERs of which:
198 have 2 to 40 ICERs
31 have ICER range
ICERs for these appraisals
are randomly drawn in
100 datasets
29. ICER data
NE: more
costly,
more
effective
SE: less
costly,
more
effective
NW:
more
costly, less
effective
SW: less
costly, less
effective
Quadrants
vary across
decisions
Number
decisions
All 418 33 31 6 22
Yes (%) 282 (67%) 33 (100%) 0 (0%) 5 (83%) 13 (59%)
No (%) 136 (33%) 0 (0%) 31 (100%) 1 (17%) 9 (41%)
Mean
ICER
All £34,207 Dominant Dominated £5,760 -
Yes £17,450 Dominant - £5,544 -
No £68,952 - Dominated £6,839 -
Average ICER is >3 times higher for ‘no’ decisions than ‘yes’
Dominance perfectly predicts NICE recommendations
Subsequent analyses focus on the NE quadrant decisions
30. Impact of ICER ranking on
recommendations
Decisions with high ICERs are more likely to be rejected, but
there are many exceptions
£0
£45,500
£2,500
£5,000
£7,500
£60,000
£70,000
£100,000
£500,000
£10,000
£12,500
£15,000
£17,500
£20,000
£22,500
£25,000
£27,500
£30,000
£32,500
£35,000
£37,500
£40,000
£45,000
£50,000
Blue = recommended; red = rejected
31. Proportion of decisions below different
thresholds that are rejected
50% of decisions with ICERs >£20,000 are rejected
32. Sensitivity and specificity at different
thresholds
ROC analysis suggests ICER strongly predicts decisions (AUC 0.85)
Sensitivity and specificity both equal 77% if Rc ~£30,000/QALY
% correctly classified plateaus at 81-82% between Rc 36k & £54k,
peaking at £47,743/QALY
Specificity, sensitivity and classification calculated using roctab
33. Plotting probability of rejection against
ICER
Inflection points at ~£20,000 and
~£50,000/QALY?
Rawlins & Culyer estimated
that the relationship was of
this shape and that 'inflexion A
occurs at around £5,000-
£15,000/QALY and inflexion B
at around £25,000-
£35,000/QALY'
Decisions are grouped into categories with similar ICER;
proportion of decisions in each category that were recommended
plotted against mid-point of each category
Curved line shows approximate best fit by eye
35. Results of the basic model (1)
Variable Definition Odds ratio (SE)
ICER Cost-effectiveness ratio (£’000s) 0.938 (0.915, 0.962)*
Total Pts in RCTs
Total number of patients randomised in
all RCTs for this decision
0.99999 (0.99996, 1.00002)
Only treatment =1 if there are no alternative treatments 2.263 (0.448, 11.448)
Children =1 if concerns treatments for children 3.774 (0.274, 52.026)
Patient group
submission
=1 if ≥1 patient group(s) made a
submission
0.929 (0.097, 8.912)
Publication date
Years since first NICE appraisal was
published
1.061 (0.947, 1.188)
Severity
Mean DALY weight for conditions in this
disease category
0.435 (0.031, 6.022)
* p<0.05
Every £1000 increase in ICER reduces odds of yes by 6.2%
No other variables are statistically significant
36. Results of the basic model (2)
At average levels for all covariates, a decision would have a
50% chance of rejection if its ICER were £45,118/QALY
37. Results of the basic model (3)
Based on a 50% cut-off, 81.63% of decisions are correctly
classified
• However, at this cut-off, sensitivity (94% of ‘yes’ decisions correctly
predicted) is higher than specificity (56% of ‘no’ decisions correct)
Actual
recommendation
No Yes Total
Predicted
recommendation
No 79 (56%) 17 (6%) 96
Yes 61 (44%) 271 (94%) 332
Total 140 288 428
38. Impact of removing variables from
basic model
Removing any variable except severity worsens prediction
accuracy compared with basic model (81.63%)
Variable removed % correctly classified after
removing variable
Total Patients in RCTs 81.53%
Only treatment 81.25%
Children 81.61%
Patient group submission 81.63%
Publication date 81.62%
Severity 81.84%
All variables except ICER 81.44%
39. Effect of adding variables to basic model
Variable Definition % correct Odds ratio
Pharmaceutical = 1 for drugs 81.62% 0.903 (p=0.81)
PSA = 1 if the model has PSA 81.78% 0.672 (p=0.45)
Broader
perspective
= 1 if non-NHS/PSS costs were analysed or
discussed
81.57% 0.666 (p=0.46)
STA = 1 if the STA process was used 81.74% 0.659 (p=0.23)
Orphan = 1 if Tx has EMEA orphan status 81.94% 1.415 (p=0.55)
Range of ICERs
Difference between the lowest and highest
NE quadrant ICER for this decision in £’000s
82.56% 0.987 (p=0.15)
Cancer
Dummy =1 if decision is for this disease
82.29% 2.025 (p=0.10)
Cardiovascular 81.70% 0.869 (p=0.75)
Central nervous
system
81.41% 0.433 (p=0.30)
Endocrine 81.58% 0.648 (p=0.45)
Infectious 81.69% 1.122 (p=0.89)
Mental health 81.50% 0.411 (p=0.49)
Musculoskeletal 81.99% 3.317 (p=0.02)
Respiratory 82.55% 0.855 (p=0.002)
40. Model including all variables increasing
prediction accuracy
Omitted severity and added STA, PSA, orphan, ICER range,
cancer, cardiovascular disease, infectious disease,
musculoskeletal & respiratory to basic model as these
improved model fit
Correctly classified 84.20% of NICE decisions
• Specificity was higher than basic model, but sensitivity was lower
Actual recommendation
No Yes Total
Predicted
recommendation
No 25 (66%) 6 (8%) 32
Yes 13 (34%) 80 (92%) 93
Total 39 86 125
41. Comparison of model predictions
Allowing for other factors has little impact on curve or threshold
ICER only: £45,449
Basic model: £45,118
Basic model minus severity, plus
STA, PSA, orphan, ICER range,
cancer, CVD, infection,
musculoskeletal & respiratory :
£41,808
42. Comparing thresholds across diseases
NICE decisions and thresholds appear to vary substantially
across diseases
43. End of life
The impact of end of life was evaluated for the 133 decisions
with draft guidance since Jan 2009
Adding end-of-life variable to basic model improves prediction
accuracy for these decisions from 84.23% to 85.12%
• More than any other variable explored except ICER range and
respiratory
Odds of NICE saying ‘yes’ are 3.37 (95% CI: 0.64, 17.86)
times higher if meets end-of-life criteria (p=0.153)
44. End of life (2)
Threshold appears to be higher (>£50,000) post-2009
End-of-life treatments have higher ICER
Not end of life: £53,534
End of life: £67,646
46. Conclusions (1)
ICER is by far the strongest predictor of NICE decisions
• Excluding those decisions based on clinical grounds or lack of evidence
ICER alone explains 82% of NICE decisions
Other variables significantly affecting NICE decisions include
• Whether for respiratory disorder (less chance of ‘yes’)
• Whether for musculoskeletal disorder (more chance of ‘yes’)
Variables improving predictions, but not statistically significant
• End of life – matches EoL guidance;
• PSA; orphan; uncertainty
• Cardiovascular disease, cancer, infection
• Committee; innovation
47. Conclusions (2)
Odds of a ‘yes’ decrease by ~6% for every £1000 increase in
ICER
50% of decisions with ICERs >£20,000/QALY are ‘no’
Specificity and sensitivity are equalised at £30,000/QALY
threshold
Our ‘best’ model suggests that the average decision with an
ICER of £42,000 has a 50% chance of being rejected
48. Next steps
Conduct sensitivity and subgroup analyses to explore how
results vary with alternative specification of models and
variables
• Further exploration of variations over time, e.g. subgrouping appraisals
by time periods
• Cross-validation to be conducted on the best model
Step function model, assuming that NICE rejects all
treatments above a certain threshold ICER?
• Additional variables increase/decrease the threshold, not the log-odds
Multi-part models of decision-making
49. Multi-part models of decision-making
Current analyses exclude decisions not based on cost-
effectiveness grounds
Some of the variables (e.g. clinical evidence or only treatment)
may predict these decisions
Decisions to reject/recommend on other grounds may occur
before ICER evidence is considered
Could explore these earlier steps in 2- or 3-part models
Rejected on clinical grounds
Recommended on clinical grounds
Consider cost-effectiveness
Rejected based on lack of clinical evidence
50. Discussion points
Which threshold is correct?
• ICER at which there is a 50% chance of rejection?
• ICER that maximises specificity or sensitivity?
• ICER above which there is a 50% chance of rejection?
Are the multi-part models realistic? Which is best?
Is there any way that we could better measure
innovation, severity and/or uncertainty?
Is the % of decisions correctly classified the best way
to select models?
• Can we pool AIC across datasets?
Is it reasonable to select models on prediction
accuracy without a validation sample?
How should we present the data?
51. Acknowledgments
We would like to thank:
A consortium of 12 companies that provided a
research grant to facilitate the initial data collection
and modelling
HTAinSite and (in particular) Carmel Guarnieri and
Zoe Philips for providing the data used in this
analysis
Members of HERC, ScHARR and HESG for their
comments on our earlier work