Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement: An Applied Approach Using SAS & STATA

Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement: An Applied Approach Using SAS & STATA

by Iftekhar Khan

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Product Details

ISBN-13: 9781466505476
Publisher: Taylor & Francis
Publication date: 11/18/2015
Series: Chapman & Hall/CRC Biostatistics Series , #87
Edition description: New Edition
Pages: 339
Product dimensions: 6.12(w) x 9.25(h) x 0.90(d)

About the Author

Iftekhar Khan is a statistician, health economics researcher and academic at University College London (University of London). He has been an applied statistician for over 15 years in clinical trials, having worked in pharmaceutical companies and academic clinical trials units. Iftekhar Khan has completed degrees in statistics and mathematics from King’s College London, the University of Kent and the University of Cambridge, including a master’s in health economics and a PhD in health economic modelling (University College London).

Table of Contents

Introduction to Economic Evaluation
Health Economics, Pharmacoeconomics, and Economic Evaluation
Important Concepts in Economic Evaluation
Health Economic Evaluation and Drug Development
Efficacy, Effectiveness and Efficiency
When Is a Pharmacoeconomic Hypothesis Possible?
Exercises

Health Economic Evaluation Concepts
Incremental Cost-Effectiveness Ratio (ICER)
Incremental INMB
The Concept of Dominance
Types of Economic Evaluation
Statistical versus Health Economic Models
Exercises
Appendix SAS/STATA Code

Designing Cost-Effectiveness into a Clinical Trial
Reasons for Collecting Economic Data in a Clinical Trial
Planning a Health Economic Evaluation in a Clinical Trial
Clinical Trial Design Issues in an Economic Evaluation
Integrating Economic Evaluation in a Clinical Trial: Considerations
CRF Design and Data Management Issues
Case Study of a Lung Cancer Trial with an Economic
Evaluation
Exercises
Appendix: SAS/STATA

Analysing Cost Data Collected in a Clinical Trial
Collecting and Measuring Costs for the Case Report Form
Types of Costs
Other Concepts in Costs: Time Horizon and Discounting
CRFs for Collecting Resource Use Data in Clinical Trials
Statistical Modelling of Cost Data
Using Generalised Linear Models to Analyse Cost Data
Models for Skewed Distributions Outside the GLM Family of Distributions
Summary of Modelling Approaches
Handling Censored and Missing Costs
Strategies for Avoiding Missing Resource Data
Strategies for Analysing Cost Data When Data Are Missing or Censored
Imputation Methods
Censored Cost Data
Method of Lin et al. (1997)
Summary and Conclusion
Exercises
Appendix: SAS/STATA Code

Quality of Life in Economic Evaluation
Quality of Life in Clinical Trials versus Quality of Life for Economic Evaluation
Disease-Specific and Generic Measures of HRQoL
HRQoL Instruments Used for the Purposes of Economic Evaluation
When HRQoL Data Have Not Been Collected in a Clinical Trial
HRQoL Metrics for Use in Economic Evaluations
Are Utility Measures Sensitive Enough for Detecting Treatment Differences?
Exercises
Appendix 5A SAS/STATA Code
Technical Appendix: Beta Binomial Technical Details
Technical Appendix: Technical Summary of the GLM

Modelling in Economic Evaluation
Introduction to Modelling: Statistical versus Economic Modelling
Decision Tree Models
Markov Modelling/Cohort Simulation
Analysis of Patient-Level Data
Patient-Level Simulation
Other Issues in Modelling
Exercises
Appendix: SAS/STATA Code

Sensitivity Analyses
Introduction to Sensitivity Analysis
One-Way Sensitivity Analysis
Two-Way Sensitivity Analysis
PSA
Bayesian Sensitivity Analyses
Issues in Interpreting and Reporting Results from Sensitivity Analysis
Exercises
Appendix: SAS/STATA Code

Sample Size and Value of Information for Cost-Effectiveness Trials
Introduction
Sample Sizes for Cost-Effectiveness
Sample Size Methods for Efficacy
Sample Size Formulae for Cost-Effectiveness: Examples
Factors Affecting Sample Sizes
The Minimum Sample Size to Establish Cost-Effectiveness
Bayesian Sample Size Approach
The Normality Assumption
Obtaining the Necessary Data and Tools for Calculating
Sample Size
Value of Information
Exercises for Chapter 8
Appendix 8A SAS/STATA Code
Technical Appendix 8B Derivation of Sample Size Formula
Technical Appendix 8C Comparison with Briggs and Tambour’s (2001) Approach

Mixed Treatment Comparisons, Evidence Synthesis
Introduction
MTCs
Meta-Analysis
Exercises
Appendix: SAS/STATA Code

Cost-Effectiveness Analyses of Cancer Trials
Introduction
Modelling Patient-Level Data from Cancer Trials for Cost-
Effectiveness
Flexible Parametric Survival Models
Modelling Survival Data Using a Flexible Parametric Model
Cost-Effectiveness of Lenalidomide
Transition Probabilities and Survival Rates
Handling Crossover (Treatment Switching) in Cancer Trials
Landmark Analysis and Presenting Survival Data by Tumour Response
Exercises
Appendix: SAS/STATA Code

The Reimbursement Environment
Regulatory Requirements for Clinical Efficacy versus Payer Requirements for Value
Reimbursement and Payer Evidence Requirements across Different Countries
Market Access and Strategy
Value-Based Pricing
Submissions for Payer Evidence
Further Areas for Research
Exercises

References

Bibliography

Index

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