Clinical trial optimization using R:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
[2017]
|
| Series: | Chapman & Hall/CRC biostatistics series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | xvii, 319 Seiten Illustrationen |
| ISBN: | 9781498735070 149873507X |
Staff View
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| 245 | 1 | 0 | |a Clinical trial optimization using R |c edited by Alex Dmitrienko, Erik Pulkstenis |
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Record in the Search Index
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| adam_text | Contents
Preface xiii
List of Contributors xvii
1 Clinical Scenario Evaluation and Clinical Trial Optimization 1
Alex Dmitrienko and Gautier Paux
1.1 Introduction ................................................ 1
1.2 Clinical Scenario Evaluation ................................. 2
1.2.1 Components of Clinical Scenario Evaluation............. 2
1.2.2 Software implementation ............................... 4
1.2.3 Case study 1.1: Clinical trial with a normally distributed
endpoint.............................................. 16
1.2.4 Case study 1.2: Clinical trial with two time-to-event
endpoints............................................. 20
1.3 Clinical trial optimization.................................. 28
1.3.1 Optimization strategies............................... 30
1.3.2 Optimization algorithm................................ 33
1.3.3 Sensitivity assessments............................... 34
1.4 Direct optimization ........................................ 38
1.4.1 Case study 1.3: Clinical trial with two patient popula-
tions ....................................................... 38
1.4.2 Qualitative sensitivity assessment.................... 43
1.4.3 Quantitative sensitivity assessment................... 44
1.4.4 Optimal selection of the target parameter............. 53
1.5 LVadeoif-based optimization ................................. 59
1.5.1 Case study 1.4: Clinical trial with an adaptive design 59
1.5.2 Optimal selection of the target parameter............. 67
2 Clinical Trials with Multiple Objectives 71
Alex Dmitrienko and Gautier Paux
2.1 Introduction ................................................ 71
2.2 Clinical Scenario Evaluation framework....................... 73
2.2.1 Data models .......................................... 74
2.2.2 Analysis models ...................................... 74
2.2.3 Evaluation models..................................... 85
2.3 Case study 2.1: Optimal selection of a multiplicity adjustment 91
ix
X
Contents
2.3.1 Clinical trial ....................................... 92
2.3.2 Qualitative sensitivity assessment .................... 99
2.3.3 Quantitative sensitivity assessment................... 107
2.3.4 Software implementation .............................. 114
2.3.5 Conclusions and extensions........................... 120
2.4 Case study 2.2: Direct selection of optimal procedure parame-
ters ............................................................. 121
2.4.1 Clinical trial ....................................... 121
2.4.2 Optimal selection of the target parameter in
Procedure B1.......................................... 133
2.4.3 Optimal selection of the target parameters in Proce-
dure B2 140
2.4.4 Sensitivity assessments............................... 145
2.4.5 Software implementation .............................. 149
2.4.6 Conclusions and extensions............................ 154
2.5 Case study 2.3: Tradeoff-based selection of optimal procedure
parameters................................................... 156
2.5.1 Clinical trial ....................................... 156
2.5.2 Optimal selection of the target parameter in
Procedure H .......................................... 161
2.5.3 Software implementation .............................. 168
2.5.4 Conclusions and extensions............................ 172
3 Subgroup Analysis in Clinical Trials 173
Alex Dmitrienko and Gautier Paux
3.1 Introduction ................................................ 173
3.2 Clinical Scenario Evaluation in confirmatory subgroup analysis 175
3.2.1 Clinical Scenario Evaluation framework................ 175
3.2.2 Multiplicity adjustments............................... 181
3.2.3 Decision-making framework.............................. 184
3.3 Case study 3.1: Optimal selection of a multiplicity adjustment 188
3.3.1 Clinical trial ....................................... 189
3.3.2 Direct optimization based on disjunctive power .... 194
3.3.3 Direct optimization based on weighted power............ 196
3.3.4 Qualitative sensitivity assessment..................... 199
3.3.5 Quantitative sensitivity assessment ................... 203
3.3.6 Software implementation ............................... 208
3.3.7 Conclusions and extensions............................. 213
3.4 Case study 3.2: Optimal selection of decision rules to support
two potential claims........................................ 215
3.4.1 Clinical trial ........................................ 215
3.4.2 Influence condition................................... 215
3.4.3 Optimal selection of the influence threshold........... 219
3.4.4 Software implementation ............................... 225
3.4.5 Conclusions and extensions............................ 228
Contents xi
3.5 Case study 3.3: Optimal selection of decision rules to support
three potential claims ...................................... 228
3.5.1 Clinical trial ....................................... 228
3.5.2 Interaction condition................................. 233
3.5.3 Optimal selection of the influence and interaction thresh-
olds ........................................................ 238
3.5.4 Software implementation .............................. 244
3.5.5 Conclusions and extensions............................ 249
4 Decision Making in Clinical Development 251
Kaushik Patra, Ming-Dauh Wang, Jianliang Zhang, Aaron Dane, Paul
Metcalfe, Paul Frewer, and Erik Pulkstenis
4.1 Introduction ................................................ 251
4.2 Clinical Scenario Evaluation in Go/No֊Go decision making and
determination of probability of success ..................... 253
4.2.1 Clinical Scenario Evaluation approach................. 253
4.2.2 Go/No-Go decision criteria............................ 255
4.2.3 Probability of success............................... 258
4.2.4 Probability of success applications................... 262
4.3 Motivating example .......................................... 264
4.3.1 Clinical trial ....................................... 265
4.3.2 Software implementation .............................. 268
4.4 Case study 4.1: Bayesian Go/No-Go decision criteria .... 269
4.4.1 Clinical trial ....................................... 269
4.4.2 General sensitivity assessments....................... 271
4.4.3 Bayesian Go/No-Go evaluation using informative priors 274
4.4.4 Sample size considerations............................ 276
4.4.5 Software implementation .............................. 279
4.4.6 Conclusions and extensions . . !.................... 282
4.5 Case study 4.2: Bayesian Go/No-Go evaluation using an alter-
native decision criterion......................................... 283
4.5.1 Clinical trial .................................... . 283
4.5.2 Software implementation .............................. 285
4.5.3 Conclusions and extensions............................ 286
4.6 Case study 4.3: Bayesian Go/No-Go evaluation in a trial with
an interim analysis ......................................... 286
4.6.1 Clinical trial ....................................... 287
4.6.2 Software implementation .............................. 289
4.6.3 Conclusions and extensions............................ 290
4.7 Case study 4.4: Decision criteria in Phase II trials based on
Probability of Success....................................... 290
4.7.1 Clinical trial ....................................... 290
4.7.2 Software implementation .............................. 292
4.7.3 Conclusions and extensions............................ 293
xii Contents
4.8 Case study 4.5: Updating POS using interim or external infor-
mation ....................................................... 294
4.8.1 Clinical trial ..................................... 295
4.8.2 Software implementation ........................... 298
Bibliography 301
Index
309
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 trad-
eoffs. A variety of common development challenges are evaluated as case stud-
ies, 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.
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 Medlmmune, 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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| spelling | Clinical trial optimization using R edited by Alex Dmitrienko, Erik Pulkstenis Boca Raton ; London ; New York CRC Press, Taylor & Francis Group [2017] xvii, 319 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC biostatistics series Includes bibliographical references and index Clinical trials Statistical methods R (Computer program language) R Programm (DE-588)4705956-4 gnd rswk-swf Forschung (DE-588)4017894-8 gnd rswk-swf Medizin (DE-588)4038243-6 gnd rswk-swf Medizin (DE-588)4038243-6 s Forschung (DE-588)4017894-8 s R Programm (DE-588)4705956-4 s DE-604 Dmitrienko, Alex edt aut Pulkstenis, Erik edt aut Erscheint auch als Online-Ausgabe, eBook 978-1-4987-3508-7 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029855777&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029855777&sequence=000002&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Dmitrienko, Alex Pulkstenis, Erik Clinical trial optimization using R Clinical trials Statistical methods R (Computer program language) R Programm (DE-588)4705956-4 gnd Forschung (DE-588)4017894-8 gnd Medizin (DE-588)4038243-6 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4017894-8 (DE-588)4038243-6 |
| title | Clinical trial optimization using R |
| title_auth | Clinical trial optimization using R |
| title_exact_search | Clinical trial optimization using R |
| title_full | Clinical trial optimization using R edited by Alex Dmitrienko, Erik Pulkstenis |
| title_fullStr | Clinical trial optimization using R edited by Alex Dmitrienko, Erik Pulkstenis |
| title_full_unstemmed | Clinical trial optimization using R edited by Alex Dmitrienko, Erik Pulkstenis |
| title_short | Clinical trial optimization using R |
| title_sort | clinical trial optimization using r |
| topic | Clinical trials Statistical methods R (Computer program language) R Programm (DE-588)4705956-4 gnd Forschung (DE-588)4017894-8 gnd Medizin (DE-588)4038243-6 gnd |
| topic_facet | Clinical trials Statistical methods R (Computer program language) R Programm Forschung Medizin |
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