1st Edition
Health Technology Assessment Using Biostatistics to Break the Barriers of Adopting New Medicines
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
Introduction
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
Summary
References
Requirements and Sources of Data to Complete an HTA
Data Requirements to Complete an HTA
Cost-Effectiveness
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
Epidemiology
Summary
References
Meta-Analysis
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
Outliers
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
Summary
References
Appendix I: Diagnostic Accuracy Measures
Appendix II: Estimation of Cohen’s Kappa Score
Network Meta-Analysis
Introduction
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
References
Bayesian Methods
Introduction
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
Summary
References
Survival Analysis
Introduction
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
Summary
References
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
Summary
References
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
Summary
References
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
Summary
References
Concluding Remarks
Academic Writing from a Biostatistician’s Point of View
Introduction
Discussion and Conclusion
Sentences and Paragraphs
Time Management for Writing
Future Research
Improving Reimbursement Submissions
Summary
References
Index
Biography
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.
