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E-raamat: 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.

Arvustused

"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

Preface xiii
List of Contributors
xvii
1 Clinical Scenario Evaluation and Clinical Trial Optimization
1(70)
Alex Dmitrienko
Gautier Paux
1.1 Introduction
1(1)
1.2 Clinical Scenario Evaluation
2(26)
1.2.1 Components of Clinical Scenario Evaluation
2(2)
1.2.2 Software implementation
4(12)
1.2.3 Case study 1.1: Clinical trial with a normally distributed endpoint
16(4)
1.2.4 Case study 1.2: Clinical trial with two time-to-event endpoints
20(8)
1.3 Clinical trial optimization
28(2)
1.3.1 Optimization strategies
30(3)
1.3.2 Optimization algorithm
33(1)
1.3.3 Sensitivity assessments
34(4)
1.4 Direct optimization
38(1)
1.4.1 Case study 1.3: Clinical trial with two patient populations
38(5)
1.4.2 Qualitative sensitivity assessment
43(1)
1.4.3 Quantitative sensitivity assessment
44(9)
1.4.4 Optimal selection of the target parameter
53(6)
1.5 Tradeoff-based optimization
59(12)
1.5.1 Case study 1.4: Clinical trial with an adaptive design
59(8)
1.5.2 Optimal selection of the target parameter
67(4)
2 Clinical Trials with Multiple Objectives
71(102)
Alex Dmitrienko
Gautier Paux
2.1 Introduction
71(2)
2.2 Clinical Scenario Evaluation framework
73(18)
2.2.1 Data models
74(1)
2.2.2 Analysis models
74(11)
2.2.3 Evaluation models
85(6)
2.3 Case study 2.1: Optimal selection of a multiplicity adjustment
91(30)
2.3.1 Clinical trial
92(7)
2.3.2 Qualitative sensitivity assessment
99(8)
2.3.3 Quantitative sensitivity assessment
107(7)
2.3.4 Software implementation
114(6)
2.3.5 Conclusions and extensions
120(1)
2.4 Case study 2.2: Direct selection of optimal procedure parameters
121(35)
2.4.1 Clinical trial
121(12)
2.4.2 Optimal selection of the target parameter in Procedure B1
133(7)
2.4.3 Optimal selection of the target parameters in Procedure B2
140(5)
2.4.4 Sensitivity assessments
145(4)
2.4.5 Software implementation
149(5)
2.4.6 Conclusions and extensions
154(2)
2.5 Case study 2.3: Tradeoff-based selection of optimal procedure parameters
156(17)
2.5.1 Clinical trial
156(5)
2.5.2 Optimal selection of the target parameter in Procedure H
161(7)
2.5.3 Software implementation
168(4)
2.5.4 Conclusions and extensions
172(1)
3 Subgroup Analysis in Clinical Trials
173(78)
Alex Dmitrienko
Gautier Paux
3.1 Introduction
173(2)
3.2 Clinical Scenario Evaluation in confirmatory subgroup analysis
175(13)
3.2.1 Clinical Scenario Evaluation framework
175(6)
3.2.2 Multiplicity adjustments
181(3)
3.2.3 Decision-making framework
184(4)
3.3 Case study .1: Optimal selection of a multiplicity adjustment
188(27)
3.3.1 Clinical trial
189(5)
3.3.2 Direct optimization based on disjunctive power
194(2)
3.3.3 Direct optimization based on weighted power
196(3)
3.3.4 Qualitative sensitivity assessment
199(4)
3.3.5 Quantitative sensitivity assessment
203(5)
3.3.6 Software implementation
208(5)
3.3.7 Conclusions and extensions
213(2)
3.4 Case study .2: Optimal selection of decision rules to support two potential claims
215(13)
3.4.1 Clinical trial
215(1)
3.4.2 Influence condition
215(4)
3.4.3 Optimal selection of the influence threshold
219(6)
3.4.4 Software implementation
225(3)
3.4.5 Conclusions and extensions
228(1)
3.5 Case study 3.3: Optimal selection of decision rules to support three potential claims
228(23)
3.5.1 Clinical trial
228(5)
3.5.2 Interaction condition
233(5)
3.5.3 Optimal selection of the influence and interaction thresholds
238(6)
3.5.4 Software implementation
244(5)
3.5.5 Conclusions and extensions
249(2)
4 Decision Making in Clinical Development
251
Kaushik Patra
Ming-Dauh Wang
Jianliang Zhang
Aaron Dane
Paul Metcalfe
Paul Frewer
Erik Pulkstenis
4.1 Introduction
251(2)
4.2 Clinical Scenario Evaluation in Go/No-Go decision making and determination of probability of success
253(11)
4.2.1 Clinical Scenario Evaluation approach
253(2)
4.2.2 Go/No-Go decision criteria
255(3)
4.2.3 Probability of success
258(4)
4.2.4 Probability of success applications
262(2)
4.3 Motivating example
264(5)
4.3.1 Clinical trial
265(3)
4.3.2 Software implementation
268(1)
4.4 Case study 4.1: Bayesian Go/No-Go decision criteria
269(14)
4.4.1 Clinical trial
269(2)
4.4.2 General sensitivity assessments
271(3)
4.4.3 Bayesian Go/No-Go evaluation using informative priors
274(2)
4.4.4 Sample size considerations
276(3)
4.4.5 Software implementation
279(3)
4.4.6 Conclusions and extensions
282(1)
4.5 Case study 4.2: Bayesian Go/No-Go evaluation using an alternative decision criterion
283(3)
4.5.1 Clinical trial
283(2)
4.5.2 Software implementation
285(1)
4.5.3 Conclusions and extensions
286(1)
4.6 Case study 4.3: Bayesian Go/No-Go evaluation in a trial with an interim analysis
286(4)
4.6.1 Clinical trial
287(2)
4.6.2 Software implementation
289(1)
4.6.3 Conclusions and extensions
290(1)
4.7 Case study 4.4: Decision criteria in Phase II trials based on Probability of Success
290(4)
4.7.1 Clinical trial
290(2)
4.7.2 Software implementation
292(1)
4.7.3 Conclusions and extensions
293(1)
4.8 Case study 4.5: Updating POS using interim or external information
294
4.8.1 Clinical trial
295(3)
4.8.2 Software implementation
298(3)
Bibliography 301(8)
Index 309
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.
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