1st Edition

Health Technology Assessment Using Biostatistics to Break the Barriers of Adopting New Medicines

By MA Hopkins, MA Goeree Copyright 2015
    276 Pages 33 B/W Illustrations
    by CRC Press

    276 Pages 33 B/W Illustrations
    by CRC Press

    The term health technology refers to drugs, devices, and programs that can improve and extend quality of life. As decision-makers struggle to find ways to reduce costs while improving health care delivery, health technology assessments (HTA) provide the evidence required to make better-informed decisions.

    This is the first book that focuses on the statistical options of HTAs, to fully capture the value of health improvements along with their associated economic consequences. After reading the book, readers will better understand why some health technologies receive regulatory or reimbursement approval while others do not, what can be done to improve the chances of approval, as well as common shortcomings of submissions for drug and device reimbursement.

    The book begins by contrasting the differences between regulatory approval and reimbursement approval. Next, it reviews the principles and steps for conducting an HTA, including the reasons why different agencies will have a different focus for their scope in the HTA.

    Supplying an accessible introduction to the various statistical options for different methods in an HTA, the book identifies the links to regulatory and reimbursement decisions for each option. It highlights many of the methodological advances that have occurred since HTA research began, to provide researchers and decision-makers with a cutting-edge framework. It also details the logical basis for the methods along with simple instructions on how to conduct the various techniques.

    Both authors have considerable experience in generating evidence for submissions and reviewing submissions to decision-makers for funding. One of the authors has also received a nationally recognized lifetime achievement award in this area.

    Regulation, Reimbursement and Health Technology Assessment
         Regulatory Approval 
              Regulatory Approval for Prescription Drugs 
              Regulatory Approval for Devices 
              Regulatory Approval for Public Health and Other Non- Drug Non-Device Approvals 
         Reimbursement Approval for Drugs 
              Initiation of Drug Review for Reimbursement
              Further Clinical Evidence for Drug Reimbursement 
              Consideration of Cost in Drug Reimbursement Decisions 
              Drug Price Negotiations 
         Reimbursement Approval for Devices 
         Health Technology Assessment 
              Step 1: Identify the Topic for Assessment 
              Step 2: Clear Specification of the Problem 
              Step 3: Gathering the Evidence 
              Step 4: Aggregation and Appraisal of the Evidence 
              Step 5: Synthesize and Consolidate Evidence 
              Step 6: Collection of Primary Data (Field Evaluation) 
              Step 7: Economic Evaluation, Budget and Health Systems Impact Analysis 
              Step 8: Assessment of Social, Ethical and Legal Considerations 
              Step 9: Formulation of Findings and Recommendations 
              Step 10: Dissemination of Findings and Recommendations 
              Step 11: Monitoring the Impact of Assessment Reports

    Requirements and Sources of Data to Complete an HTA
    Data Requirements to Complete an HTA
    Introduction to Health-Related Quality of Life
    Introduction to Resource Utilization and Costs
    Need for Modelling 
         Decision Analytic Model 
         Markov Model
    Start with the Trials: Safety and Efficacy
    Secondary Data Requirements 
         Rare Diseases 
         Effectiveness versus Efficacy 
         Long-Term Outcomes 
         Health-Related Quality of Life 
         Resource Utilization and Costs 

    Overview of Meta-Analysis
    Initial Steps before a Meta-Analysis
    A Comment on Frequentist and Bayesian Approaches
    Steps in a Meta-Analysis
    Step 1: Identify the Type of Data for Each Outcome
    Step 2: Select an Appropriate Outcome Measure 
         Outcomes for Continuous Data
    Step 3: Conduct the Preliminary Analysis with an Assessment of Heterogeneity 
         Weighting of Each Study 
         Random or Fixed Effects 
         Testing for Heterogeneity
    Step 4: Adjustment for Heterogeneity
    Step 5: Assess Publication Bias
    Step 6: Assess the Overall Strength of Evidence
    An Example of Meta-Analysis 
         Risk-Adjusted or Unadjusted Analysis 
         Publication Bias
    Meta-Analysis of Diagnostic Accuracy Studies
    Example of Meta-Analysis for Diagnostic Accuracy 
         Hierarchical Summary Receiver Operator Curve
    Appendix I: Diagnostic Accuracy Measures
    Appendix II: Estimation of Cohen’s Kappa Score

    Network Meta-Analysis
         Head-to-Head and Placebo-Controlled Trials
    Step 1: Establish Potential Network Diagram of Linking Studies
    Step 2: Check for Consistency in Outcomes for Common Linking Arms
    Step 3: Conduct Meta-Analysis and Assess Heterogeneity within Common Comparators
    Step 4: Conduct Indirect Meta-Analysis across the Comparators 
         Network Meta-Analysis Software
    Step 5: Conduct Subgroup and Sensitivity Analyses
    Step 6: Report Network Meta-Analysis Results
    Bayesian Mixed Treatment Comparisons
    Network Meta-Analysis Example 
         Assessing Robustness: Homogeneity and Consistency of Evidence 
         Adjustment for Difference in Baseline Characteristics
    Network Meta-Analysis of Diagnostic Accuracy

    Bayesian Methods
         Study Power for Trials of Rare Diseases 
         Interpretation of Bayesian Results
    Bayesian Theorem
    Step 1: Specify the Model
    Step 2: Assign the Prior(s)
    Step 3: Conduct the Simulation
    Step 4: Assess Convergence
    Step 5: Report the Findings
    Advanced Bayesian Models 
         Advanced Example 1: Combining RCTs and Observational Data 
         Advanced Example 2: Covariate Adjustment 
         Advanced Example 3: Hierarchical Outcomes

    Survival Analysis
    Kaplan–Meier Analysis
    Exponential, Gompertz and Weibull Models
    Establishing and Using Risk Equations 
         Diabetes Modelling
    Acceptability of Surrogates
    Survival Adjustment for Crossover Bias
    Building a Life Table from Cross-Sectional Data

    Costs and Cost of Illness Studies
    From Clinical Events to Resource Utilization to Costs 
         Measurement of Resource Utilization
    Attribution and Adjustment for Comorbidities 
         Strategies to Isolate the Cost of an Event 
         Regression Methods
         Other Strategies to Estimate Costs 
         Unit Costs Valuation for Resources
    Perspective and Types of Costs
    Burden of Illness Study
    Budget Impact Analysis 
         Statistical Issues with Cost Data

    Health-Related Quality of Life

    Why QOL?
    Good Properties of Scales
    Guidelines for Using QOL in HTA
    From Utility to QALY
    Assessing Change in QOL Scales 
         Change in Level of HRQOL and Domains over Time 
         Minimal Clinically Important Difference for HRQOL
    Obtaining QOL Estimates from Trials and Literature
    Independent QOL Study
    Mapping between QOL Scales

    Missing Data Methods
    Common Trial Gaps 
         Missed Visits and Loss to Follow-Up 
         Explainable or Unexplainable Patterns of Missing Data 
         Intention-to-Treat or Per-Protocol Analysis 
         Multiple Imputation for Trial Data 
              Beautiful Bootstrap 
    Meta-Analysis Gaps 
         Missing Measures of Central Tendency 
         Missing Measures of Variance 
         Missing Data for Diagnostic Accuracy Studies
    Unknown Lifetime Variances for Costs

    Concluding Remarks
    Academic Writing from a Biostatistician’s Point of View 
         Discussion and Conclusion 
         Sentences and Paragraphs 
         Time Management for Writing
    Future Research
    Improving Reimbursement Submissions



    Robert Borden Hopkins, PhD, has been the biostatistician at the Programs for the Assessment of Technology in Health (PATH) Research Institute at McMaster University for the past 10 years and has more than 25 years of experience in health care. His role as the biostatistician continues to include educational support at the graduate level; designing and analyzing systematic reviews; designing, conducting and analyzing clinical studies (field evaluations); conducting economic evaluations, burden of illness studies and health technology assessments and providing peer review for more than 20 academic journals and government agencies.

    Rob was the lead biostatistician for more than 75 funded research projects worth over $15 million, which generated over 100 peer-reviewed publications and abstracts and 40 technical reports for the government, as well over 200 conference, academic or government presentations. Recent methodological issues explored include handling of missing data in meta-analysis, trials and economic evaluations; network meta- analysis; trial-based economic analysis and cost/burden of illness studies.

    Rob has presented his research at the following conferences: Society of Medical Decision Making, International Society for Pharmacoeconomics and Outcomes Research, Drug Information Association, Canadian Association for Population Therapeutics, Canadian Agency for Drugs and Technologies in Health (CADTH), Canadian Association for Health Services and Policy Research, Society for Clinical Trials, Health Technology Assessment International, Canadian Statistical Society, American Statistical Society, Canadian Health Economics Association and International Health Economics Association.

    Ron Goeree, MA, is currently a professor in the Department of Clinical Epidemiology & Biostatistics, Faculty of Health Sciences, at McMaster University in Hamilton, Ontario, Canada, where he is the founding field leader for graduate studies of health technology assessment (HTA) at McMaster University.

    Ron has established workshops on HTA all over the world, from Singapore to Oslo, and has published extensively (over 400 books, chapters, articles and abstracts). He has reviewed over 120 journal submissions and 80 national or provincial drug submissions or reports; Ron has served on nearly 50 industry advisory boards and more than 60 government/decision-maker committees and boards.

    Ron’s research is conducted at the Programs for Assessment of Health Technology Research Institute at St. Joseph’s Healthcare Hamilton, where he has been the director since 2006. ‘As director of PATH, he has demonstrated the essential role health technology assessment can and should play in meeting the needs of health of health decision-makers. As an innovator, he helped pioneer the methodological framework for the field evaluation of non-drug technologies. As a dedicated professor and mentor, he has trained literally thousands of students, researchers, and decision-makers, making an immense contribution to the capacity in Canada to produce and use health technology assessment’, said O’Rourke, President and CEO of CADTH.

    O’Rourke further said that ‘Professor Goeree is one of the pre-eminent HTA researchers and educators in the world’ (CADTH News Release 2012). Ron was the 2012 recipient of the CADTH HTA Excellence Award for lifetime and sustained achievement; he is co-editor of Value in Health and sits on the editorial boards of Medical Decision Making and the Journal of Medical Economics.