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Clinical Trial Optimization Using R
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Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies. It provides the clinical researcher with a powerful evaluation paradigm, as well as supportive R tools, to evaluate and select among simultaneous competing designs or analysis options. It is applicable broadly to statisticians and other quantitative clinical trialists, who have an interest in optimizing clinical trials, clinical trial programs, or associated analytics and decision making.
This book presents in depth the Clinical Scenario Evaluation (CSE) framework, and discusses optimization strategies, including the quantitative assessment of tradeoffs. A variety of common development challenges are evaluated as case studies, and used to show how this framework both simplifies and optimizes strategy selection. Specific settings include optimizing adaptive designs, multiplicity and subgroup analysis strategies, and overall development decision-making criteria around Go/No-Go. After this book, the reader will be equipped to extend the CSE framework to their particular development challenges as well.
Clinical Scenario Evaluation and Clinical Trial Optimization
Alex Dmitrienko and Gautier Paux
Introduction
Clinical scenario evaluation
Components of Clinical Scenario Evaluation
Software implementation
Case study 1.1: Clinical trial with a normally distributed endpoint
Case study 1.2: Clinical trial with two time-to-event endpoints
Clinical trial optimization
Optimization strategies
Optimization algorithm
Sensitivity assessments
Direct optimization
Case study 1.3: Clinical trial with two patient populations
Qualitative sensitivity assessment
Quantitative sensitivity assessment
Optimal selection of the target parameter
Tradeoff-based optimization
Case study 1.4: Clinical trial with an adaptive design
Optimal selection of the target parameter
Clinical Trials with Multiple Objectives
Alex Dmitrienko and Gautier Paux
Introduction
Clinical Scenario Evaluation framework
Case study 2.1: Optimal selection of a multiplicity adjustment
Qualitative sensitivity assessment
Quantitative sensitivity assessment
Software implementation
Conclusions and extensions
Case study 2.2: Direct selection of optimal procedure parameters
Case study 2.3: Tradeoff-based selection of optimal procedure parameters
Clinical trial
Subgroup Analysis in Clinical Trials
Alex Dmitrienko and Gautier Paux
Introduction
Clinical Scenario Evaluation in confirmatory subgroup analysis
Case study 3.1: Optimal selection of a multiplicity adjustment
Case study 3.2: Optimal selection of decision rules to support two potential claims
Case study 3.3: Optimal selection of decision rules to support three potential claims
Decision Making in Clinical Development
Kaushik Patra, Ming-Dauh Wang, Jianliang Zhang, Aaron Dane, Paul Metcalfe, Paul Frewer, and Erik Pulkstenis
Introduction
Clinical Scenario Evaluation in Go/No-Go decision making and determination of probability of success
Case study 4.1: Bayesian Go/No-Go decision criteria
Case study 4.2: Bayesian Go/No-Go evaluation using an alternative decision criterion
Case study 4.3: Bayesian Go/No-Go evaluation in a trial with an interim analysis
Case study 4.4: Decision criteria in Phase II trials based on Probability of Success
Case study 4.5: Updating POS using interim or external information
Bibliography
Biography
Alex Dmitrienko is President at Mediana Inc. He has been actively involved in biostatistical research with emphasis on multiplicity issues in clinical trials, subgroup analysis, innovative trial designs and clinical trial optimization. He has published over 90 papers and authored/edited three books. Dr. Dmitrienko is a Fellow of the American Statistical Association.
Erik Pulkstenis is Vice President, Clinical Biostatistics and Data Management at MedImmune, and has worked in the medical device and biopharmaceutical industry for over 20 years. In addition, he served as a faculty member for the Institute for Professional Education teaching on categorical data analysis. His research interests include evidence-based decision making, precision medicine, and applications of statistical methods in oncology.
"The book Clinical Trial Optimization Using R by A. Dmitrienko and E. Pulkstenis gives a comprehensible introduction to the subject of Clinical Scenario Evaluation (CSE) and subsequent optimization . . . The authors present an approach that is easy to understand and to implement in R. The book is well structured, and the underlying principles are described in detail and illustrated by several case studies."
~ Kiana Kreitz, Institute of Biostatistics and Clinical Research, Germany
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