Clinical Trial Optimization Using R: 1st Edition (Hardback) book cover

Clinical Trial Optimization Using R

1st Edition

Edited by Alex Dmitrienko, Erik Pulkstenis

Chapman and Hall/CRC

319 pages | 100 B/W Illus.

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Hardback: 9781498735070
pub: 2017-06-07
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Description

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.

Reviews

"The book Clinical Trial Optimization Using Rby 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

Table of Contents

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

About the Editors

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.

About the Series

Chapman & Hall/CRC Biostatistics Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
MED071000
MEDICAL / Pharmacology
MED090000
MEDICAL / Biostatistics