Clinical Trial Optimization Using R

✍ Scribed by Alex Dmitrienko, Erik Pulkstenis

• Publisher: CRC Press;Chapman and Hall/CRC
• Year: 2017
• Tongue: English
• Leaves: 338
• Series: Chapman & Hall/CRC Biostatistics Series
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Content: Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
List of Contributors
1: Clinical Scenario Evaluation and Clinical Trial Optimization
1.1 Introduction
1.2 Clinical Scenario Evaluation
1.2.1 Components of Clinical Scenario Evaluation 1.2.2 Software implementation 1.2.3 Case study 1.1: Clinical trial with a normally distributed endpoint
1.2.4 Case study 1.2: Clinical trial with two time-to-event endpoints
1.3 Clinical trial optimization
1.3.1 Optimization strategies
1.3.2 Optimization algorithm 1.3.3 Sensitivity assessments 1.4 Direct optimization
1.4.1 Case study 1.3: Clinical trial with two patient populations
1.4.2 Qualitative sensitivity assessment
1.4.3 Quantitative sensitivity assessment
1.4.4 Optimal selection of the target parameter 1.5 Tradeoff-based optimization 1.5.1 Case study 1.4: Clinical trial with an adaptive design
1.5.2 Optimal selection of the target parameter
2: Clinical Trials with Multiple Objectives
2.1 Introduction
2.2 Clinical Scenario Evaluation framework
2.2.1 Data models 2.2.2 Analysis models 2.2.3 Evaluation models
2.3 Case study 2.1: Optimal selection of a multiplicity adjustment
2.3.1 Clinical trial
2.3.2 Qualitative sensitivity assessment
2.3.3 Quantitative sensitivity assessment
2.3.4 Software implementation

✦ Subjects

Clinical trials -- Statistical methods;R (Computer program language);HEALTH & FITNESS / Holism;HEALTH & FITNESS / Reference;MEDICAL / Alternative Medicine;MEDICAL / Atlases;MEDICAL / Essays;MEDICAL / Family & General Practice;MEDICAL / Holistic Medicine;MEDICAL / Osteopathy