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E-raamat: Health Technology Assessment: Using Biostatistics to Break the Barriers of Adopting New Medicines

(McMaster University, Hamilton, Ontario, Canada),
  • Formaat: 276 pages
  • Ilmumisaeg: 10-Apr-2015
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781482244533
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Health Technology Assessment: Using Biostatistics to Break the Barriers of Adopting New MedicinesSuurem pilt
  • Formaat: 276 pages
  • Ilmumisaeg: 10-Apr-2015
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781482244533
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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.
Authors xi
Preface xiii
1 Regulation, Reimbursement and Health Technology Assessment 1(36)
Introduction
1(1)
Regulatory Approval
2(11)
Regulatory Approval for Prescription Drugs
3(7)
Regulatory Approval for Devices
10(2)
Regulatory Approval for Public Health and Other Non-Drug, Non-Device Approvals
12(1)
Reimbursement Approval for Drugs
13(3)
Initiation of Drug Review for Reimbursement
14(1)
Further Clinical Evidence for Drug Reimbursement
14(1)
Consideration of Cost in Drug Reimbursement Decisions
15(1)
Drug Price Negotiations
15(1)
Reimbursement Approval for Devices
16(1)
Health Technology Assessment
17(16)
Step 1: Identify the Topic for Assessment
20(2)
Step 2: Clear Specification of the Problem
22(2)
Step 3: Gathering the Evidence
24(1)
Step 4: Aggregation and Appraisal of the Evidence
25(2)
Step 5: Synthesize and Consolidate Evidence
27(1)
Step 6: Collection of Primary Data (Field Evaluation)
28(1)
Step 7: Economic Evaluation, Budget and Health Systems Impact Analysis
29(1)
Step 8: Assessment of Social, Ethical and Legal Considerations
30(1)
Step 9: Formulation of Findings and Recommendations
30(2)
Step 10: Dissemination of Findings and Recommendations
32(1)
Step 11: Monitoring the Impact of Assessment Reports
32(1)
Summary
33(1)
References
33(4)
2 Requirements and Sources of Data to Complete an HTA 37(28)
Data Requirements to Complete an HTA
37(1)
Cost-Effectiveness
37(4)
Introduction to Health-Related Quality of Life
41(3)
Introduction to Resource Utilization and Costs
44(2)
Need for Modelling
46(3)
Decision Analytic Model
47(1)
Markov Model
48(1)
Start with the Trials: Safety and Efficacy
49(1)
Secondary Data Requirements
50(10)
Rare Diseases
52(1)
Effectiveness versus Efficacy
53(1)
Long-Term Outcomes
54(1)
Health-Related Quality of Life
55(1)
Resource Utilization and Costs
56(3)
Epidemiology
59(1)
Summary
60(1)
References
60(5)
3 Meta-Analysis 65(40)
Overview of Meta-Analysis
65(2)
Initial Steps before a Meta-Analysis
67(1)
A Comment on Frequentist and Bayesian Approaches
68(1)
Steps in a Meta-Analysis
69(1)
Step 1: Identify the Type of Data for Each Outcome
70(2)
Step 2: Select an Appropriate Outcome Measure
72(3)
Outcomes for Continuous Data
74(1)
Step 3: Conduct the Preliminary Analysis with an Assessment of Heterogeneity
75(3)
Weighting of Each Study
75(1)
Random or Fixed Effects
76(1)
Testing for Heterogeneity
77(1)
Step 4: Adjustment for Heterogeneity
78(2)
Step 5: Assess Publication Bias
80(1)
Step 6: Assess the Overall Strength of Evidence
81(1)
An Example of Meta-Analysis
82(8)
Outliers
86(1)
Risk-Adjusted or Unadjusted Analysis
87(2)
Publication Bias
89(1)
Meta-Analysis of Diagnostic Accuracy Studies
90(5)
Example of Meta-Analysis for Diagnostic Accuracy
95(6)
Hierarchical Summary Receiver Operator Curve
99(2)
Summary
101(1)
References
101(2)
Appendix I: Diagnostic Accuracy Measures
103(1)
Appendix II: Estimation of Cohen's Kappa Score
104(1)
4 Network Meta-Analysis 105(22)
Introduction
105(5)
Head-to-Head and Placebo-Controlled Trials
105(5)
Step 1: Establish Potential Network Diagram of Linking Studies
110(2)
Step 2: Check for Consistency in Outcomes for Common Linking Arms
112(2)
Step 3: Conduct Meta-Analysis and Assess Heterogeneity within Common Comparators
114(2)
Step 4: Conduct Indirect Meta-Analysis across the Comparators
116(4)
Network Meta-Analysis Software
116(4)
Step 5: Conduct Subgroup and Sensitivity Analyses
120(1)
Step 6: Report Network Meta-Analysis Results
121(1)
Bayesian Mixed Treatment Comparisons
122(1)
Network Meta-Analysis Example
122(2)
Assessing Robustness: Homogeneity and Consistency of Evidence
123(1)
Adjustment for Difference in Baseline Characteristics
123(1)
Network Meta-Analysis of Diagnostic Accuracy
124(1)
References
125(2)
5 Bayesian Methods 127(22)
Introduction
127(3)
Study Power for Trials of Rare Diseases
128(1)
Interpretation of Bayesian Results
129(1)
Bayesian Theorem
130(1)
Step 1: Specify the Model
131(3)
Step 2: Assign the Prior(s)
134(1)
Step 3: Conduct the Simulation
135(2)
Step 4: Assess Convergence
137(5)
Step 5: Report the Findings
142(1)
Advanced Bayesian Models
143(3)
Advanced Example 1: Combining RCTs and Observational Data
144(1)
Advanced Example 2: Covariate Adjustment
144(1)
Advanced Example 3: Hierarchical Outcomes
145(1)
Summary
146(1)
References
147(2)
6 Survival Analysis 149(30)
Introduction
149(1)
Kaplan-Meier Analysis
150(6)
Exponential, Gompertz and Weibull Models
156(6)
Establishing and Using Risk Equations
162(7)
Diabetes Modelling
168(1)
Acceptability of Surrogates
169(1)
Survival Adjustment for Crossover Bias
170(3)
Building a Life Table from Cross-Sectional Data
173(2)
Summary
175(1)
References
175(4)
7 Costs and Cost of Illness Studies 179(20)
From Clinical Events to Resource Utilization to Costs
180(2)
Measurement of Resource Utilization
181(1)
Attribution and Adjustment for Comorbidities
182(7)
Strategies to Isolate the Cost of an Event
184(2)
Regression Methods
186(1)
Other Strategies to Estimate Costs
186(2)
Unit Costs Valuation for Resources
188(1)
Perspective and Types of Costs
189(2)
Burden of Illness Study
191(2)
Budget Impact Analysis
193(2)
Statistical Issues with Cost Data
194(1)
Summary
195(1)
References
196(3)
8 Health-Related Quality of Life 199(16)
Why QOL?
200(2)
Good Properties of Scales
202(2)
Guidelines for Using QOL in HTA
204(1)
From Utility to QALY
204(1)
Assessing Change in QOL Scales
205(4)
Change in Level of HRQOL and Domains over Time
205(2)
Minimal Clinically Important Difference for HRQOL
207(2)
Obtaining QOL Estimates from Trials and Literature
209(1)
Independent QOL Study
210(1)
Mapping between QOL Scales
211(1)
Summary
212(1)
References
213(2)
9 Missing Data Methods 215(16)
Common Trial Gaps
216(7)
Missed Visits and Loss to Follow-Up
217(1)
Explainable or Unexplainable Patterns of Missing Data
218(1)
Intention-to-Treat or Per-Protocol Analysis
219(2)
Multiple Imputation for Trial Data
221(2)
Beautiful Bootstrap
223(1)
Meta-Analysis Gaps
223(5)
Missing Measures of Central Tendency
224(1)
Missing Measures of Variance
224(3)
Missing Data for Diagnostic Accuracy Studies
227(1)
Unknown Lifetime Variances for Costs
228(2)
Summary
230(1)
References
230(1)
10 Concluding Remarks 231(12)
Concluding Remarks
231(1)
Academic Writing from a Biostatistician's Point of View
232(4)
Introduction
233(1)
Discussion and Conclusion
234(1)
Sentences and Paragraphs
235(1)
Time Management for Writing
236(1)
Future Research
236(3)
Improving Reimbursement Submissions
239(2)
Summary
241(1)
References
242(1)
Index 243
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.

Rons research is conducted at the Programs for Assessment of Health Technology Research Institute at St. Josephs 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 ORourke, President and CEO of CADTH.

ORourke 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.
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