Bayesian adaptive methods for clinical trials:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
CRC Press
2011
|
| Schriftenreihe: | Chapman & Hall/CRC biostatistics series
38 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Includes bibliographical references and indexes |
| Beschreibung: | XVII, 305 S. zahlr. graph. Darst. |
| ISBN: | 9781439825488 1439825483 |
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| 245 | 1 | 0 | |a Bayesian adaptive methods for clinical trials |c Scott M. Berry ... |
| 264 | 1 | |a Boca Raton, Fla. [u.a.] |b CRC Press |c 2011 | |
| 300 | |a XVII, 305 S. |b zahlr. graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 1 | |a Chapman & Hall/CRC biostatistics series |v 38 | |
| 500 | |a Includes bibliographical references and indexes | ||
| 650 | 4 | |a Clinical trials |x Statistical methods | |
| 650 | 4 | |a Bayesian statistical decision theory | |
| 650 | 4 | |a Clinical Trials as Topic | |
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Datensatz im Suchindex
| _version_ | 1804143301935235072 |
|---|---|
| adam_text | Contents
Poreword xi
Preface
xiii
1
Statistical approaches for clinical trials
1
1.1
Introduction
1
1.2
Comparisons between Bayesian and
frequentisi
approaches
4
1.3
Adaptivity in clinical trials
6
1.4
Features and use of the Bayesian adaptive approach
8
1.4.1
The fully Bayesian approach
8
1.4.2
Bayes as a
frequentist tool
10
1.4.3
Examples of the Bayesian approach to drug and
medical device development
12
2
Basics of Bayesian inference
19
2.1
Introduction to
Bayes
Theorem
19
2.2
Bayesian inference
26
2.2.1
Point estimation
26
2.2.2
Interval estimation
27
2.2.3
Hypothesis testing and model choice
29
2.2.4
Prediction
34
2.2.5
Effect of the prior: sensitivity analysis
37
2.2.6
Role of randomization
38
2.2.7
Handling multiplicities
40
2.3
Bayesian computation
42
2.3.1
The Gibbs sampler
44
2.3.2
The Metropolis-Hastings algorithm
45
2.3.3
Convergence diagnosis
48
2.3.4
Variance estimation
49
2.4
Hierarchical modeling and metaanalysis
51
2.5
Principles of Bayesian clinical trial design
63
2.5.1
Bayesian predictive probability methods
64
CONTENTS
Bayesian indifference zone methods
66
Prior determination
68
Operating characteristics
70
Incorporating costs
78
Delayed response
81
Noncompliance and causal modeling
82
rïï-v·
Ρ
Л/Тїзґтгіс
86
2.5.2
2.5.3
2.5.4
2.5.5
2.5.6
2.5.7
2.6
Appe:
Phase I studies 87
3.1
Rule-based designs for determining the MTD
88
3.1.1
Traditional
3+3
design
88
3.1.2
Pharmacologically guided dose escalation
91
3.1.3
Accelerated titration designs
92
3.1.4
Other rule-based designs
92
3.1.5
Summary of rule-based designs
92
3.2
Model-based designs for determining the MTD
93
3.2.1
Continual reassessment method (CRM)
94
3.2.2
Escalation with overdose control (EWOC)
102
3.2.3
Time-to-event
(ΤΙΤΕ)
monitoring
105
3.2.4
Toxicity
intervals
109
3.2.5
Ordinal
toxicity
intervals
113
3.3
Efficacy versus
toxicity
116
3.3.1
Trial parameters
117
3.3.2
Joint probability model for efficacy and
toxicity
117
3.3.3
Defining the acceptable dose levels
118
3.3.4
Efficacy-toxicity trade-off contours
118
3.4
Combination therapy
121
3.4.1
Basic Gumbel model
122
3.4.2
Divariate CRM
126
3.4.3
Combination therapy with bivariate response
127
3.4.4
Dose escalation with two agents
129
3.5
Appendix:
R
Macros
134
Phase II studies
137
4.1
Standard designs
137
4.1.1
Phase IIA designs
138
4.1.2
Phase IIB designs
140
4.1.3
Limitations of traditional
frequentisi
designs
142
4.2
Predictive probability
142
4.2.1
Definition and basic calculations for binary data
143
4.2.2
Derivation of the predictive process design
146
4.3
Sequential stopping
150
4.3.1
Binary stopping for futility and efficacy
150
4.3.2
Binary stopping for futility, efficacy, and
toxicity
151
CONTENTS xi
4.3.3 Monitoring
event times
154
4.4
Adaptive randomization and dose allocation
155
4.4.1
Principles of adaptive randomization
155
4.4.2
Dose ranging and optimal biologic dosing
163
4.4.3
Adaptive randomization in dose finding
167
4.4.4
Outcome adaptive randomization with delayed
survival response
168
4.5
Hierarchical models for phase II designs
173
4.6
Decision theoretic designs
176
4.6.1
Utility functions and their specification
176
4.6.2
Screening designs for drug development
179
4.7
Case studies in phase II adaptive design
183
4.7.1
The BATTLE trial
183
4.7.2
The I-SPY
2
trial
189
4.8
Appendix:
R
Macros
191
5
Phase III studies
193
5.1
Introduction to confirmatory studies
193
5.2
Bayesian adaptive confirmatory trials
195
5.2.1
Adaptive sample size using posterior probabilities
196
5.2.2
Futility analyses using predictive probabilities
200
5.2.3
Handling delayed outcomes
204
5.3
Arm dropping
208
5.4
Modeling and prediction
211
5.5
Prior distributions and the paradigm clash
218
5.6
Phase III cancer trials
221
5.7
Phase II/III seamless trials
228
5.7.1
Example phase II/III trial
230
5.7.2
Adaptive design
231
5.7.3
Statistical modeling
232
5.7.4
Calculation
233
5.7.5
Simulations
235
5.8
Case study: Ablation device to treat atrial fibrillation
241
5.9
Appendix:
R
Macros
247
6
Special topics
249
6.1
Incorporating historical data
249
6.1.1
Standard hierarchical models
250
6.1.2
Hierarchical power prior models
252
6.2
Equivalence studies
260
6.2.1
Statistical issues in bioequivalence
261
6.2.2
Binomial response design
263
6.2.3 2x2
crossover design
265
6.3
Multiplicity
268
xii CONTENTS
6.3.1
Assessing drug safety
269
6.3.2
Multiplicities and false discovery rate (FDR)
275
6.4
Subgroup analysis
276
6.4.1
Bayesian approach
276
6.4.2
Bayesian decision theoretic approach
277
6.5
Appendix:
R
Macros
280
References
281
Author index
297
Index
303
Already popular in the analysis of medical device trials, adaptive Bayesian
designs are increasingly being used in drug development for a wide variety
of diseases and conditions, from Alzheimer s disease and multiple sclerosis
to obesity, diabetes, hepatitis C, and
HIV.
Written by leading pioneers of
Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical
Trials explores the growing role of Bayesian thinking in the rapidly changing
world of clinical trial analysis.
The book establishes the basic principles before extending them to specific
phases and endpoints. It first summarizes the current state of clinical trial
design and analysis and introduces the main ideas and potential benefits of
a Bayesian alternative. The text then gives an overview of basic Bayesian
methodological and computational tools needed for Bayesian clinical trials.
With a focus on Bayesian designs that achieve good power and Type I
error, the next chapters present Bayesian tools useful in early (Phase
1)
and middle (Phase II) clinical trials as well as two recent Bayesian adaptive
Phase II studies: the BATTLE and ISPY-2 trials. In the following chapter on
late (Phase III) studies, the authors emphasize modern adaptive methods
and seamless Phase ll-lll trials for maximizing information usage and
minimizing trial duration. They also describe a case study of a recently
approved medical device to treat atrial fibrillation. The concluding chapter
covers key special topics, such as the proper use of historical data,
equivalence studies, and subgroup analysis.
For readers involved in clinical trials research, this book significantly
updates and expands their statistical toolkits. The authors provide many
detailed examples drawing on real data sets. The
R
and WinBUGS codes
used throughout are available on supporting Web sites.
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| id | DE-604.BV036675420 |
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| indexdate | 2024-07-09T22:45:30Z |
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| isbn | 9781439825488 1439825483 |
| language | English |
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| spelling | Bayesian adaptive methods for clinical trials Scott M. Berry ... Boca Raton, Fla. [u.a.] CRC Press 2011 XVII, 305 S. zahlr. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC biostatistics series 38 Includes bibliographical references and indexes Clinical trials Statistical methods Bayesian statistical decision theory Clinical Trials as Topic Bayes Theorem Klinisches Experiment (DE-588)4164223-5 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Klinisches Experiment (DE-588)4164223-5 s Bayes-Verfahren (DE-588)4204326-8 s b DE-604 Berry, Scott M. Sonstige oth Chapman & Hall/CRC biostatistics series 38 (DE-604)BV023097394 38 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020594415&sequence=000005&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020594415&sequence=000006&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Bayesian adaptive methods for clinical trials Chapman & Hall/CRC biostatistics series Clinical trials Statistical methods Bayesian statistical decision theory Clinical Trials as Topic Bayes Theorem Klinisches Experiment (DE-588)4164223-5 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| subject_GND | (DE-588)4164223-5 (DE-588)4204326-8 |
| title | Bayesian adaptive methods for clinical trials |
| title_auth | Bayesian adaptive methods for clinical trials |
| title_exact_search | Bayesian adaptive methods for clinical trials |
| title_full | Bayesian adaptive methods for clinical trials Scott M. Berry ... |
| title_fullStr | Bayesian adaptive methods for clinical trials Scott M. Berry ... |
| title_full_unstemmed | Bayesian adaptive methods for clinical trials Scott M. Berry ... |
| title_short | Bayesian adaptive methods for clinical trials |
| title_sort | bayesian adaptive methods for clinical trials |
| topic | Clinical trials Statistical methods Bayesian statistical decision theory Clinical Trials as Topic Bayes Theorem Klinisches Experiment (DE-588)4164223-5 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| topic_facet | Clinical trials Statistical methods Bayesian statistical decision theory Clinical Trials as Topic Bayes Theorem Klinisches Experiment Bayes-Verfahren |
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