Statistical monitoring of clinical trials: a unified approach
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer
2006
|
| Schriftenreihe: | Statistics for biology and health
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis Klappentext |
| Beschreibung: | XIII, 258 S. graph. Darst. |
| ISBN: | 0387300597 9780387300597 |
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| 100 | 1 | |a Proschan, Michael A. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Statistical monitoring of clinical trials |b a unified approach |c Michael A. Proschan ; K. K. Gordon Lan ; Janet Turk Wittes |
| 264 | 1 | |a New York, NY |b Springer |c 2006 | |
| 300 | |a XIII, 258 S. |b graph. Darst. | ||
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| 650 | 4 | |a Bayes Theorem | |
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| 650 | 4 | |a Data Interpretation, Statistical | |
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Datensatz im Suchindex
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| adam_text | Contents
Introduction
A General Framework
2.1
Over Time
2.1.1
2.1.2
2.1.3
2.1.4
2.2
2.2.1
2.2.2
2.3
Brownian Motion
2.4
2.5
2.5.1
2.5.2
2.6
2.6.1
2.6.2
2.7
2.8
2.9
2.9.1
Standard Errors
2.9.2
2.9.3
Uncorrelated Under Ho
2.9.4
Drift: Local Alternatives
Contents
2.9.5 Basu s Theorem................................... 42
Power:
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.7.1
3.7.2
3.7.3
Historical Monitoring Boundaries
4.1
4.2
4.3
4.4
4.5
4.6
4.7
Integration
Spending Functions
5.1
5.1.1
5.1.2
5.2
5.3
5.4
5.4.1
5.4.2
5.4.3
Practical Survival Monitoring
6.1
6.2
6.3
6.4
6.5
a t-Test
6.6
6.7
Contents
7
7.1
7.2
7.2.1
7.2.2
7.2.3
7.3
7.4
7.5
7.6
7.7
One-Tailed Setting
8
8.1
8.2
8.2.1
8.2.2
8.3
8.4
8.5
8.5.1
Information Time
8.5.2
9
9.1
9.2
9.3
9.4
9.5
9.5.1
9.5.2
that Would Stop a Study
9.5.3
Should Report
9.6
9.7
10
10.1
10.2
10.3
10.4
10.5
XII Contents
10.6
11
11.1
11.2
Continuous Outcome Case
11.2.1
11.2.2
11.2.3
11.2.4
11.2.5
11.2.6
11.3
Outcome Case
11.3.1
11.4
11.4.1
11.4.2
11.5
12
12.1
12.2
12.3
12.4
12.5
12.6
12.7
13
13.1
13.2
13.2.1
13.2.2
13.3
13.4
13.5
13.6
13.7
14
14.1
14.2
14.3
14.3.1
Contents XIII
14.4 After
Confidence Interval
14.5
References
Index
The approach taken in this book is, to studies monitored over time, what the Central
Limit Theorem is to studies with only one analysis. Just as the Central Limit Theorem
shows that test statistics involving very different types of clinical trial outcomes are
asymptotically normal, this book shows that the joint distribution of the test statistics at
different analysis times is asymptotically multivariate normal with the correlation struc¬
ture of Brownian motion ( the B-value ) irrespective of the test statistic. The so-called
B-value approach to monitoring allows us to use, for different types of trials, the same
boundaries and the same simple formula for computing conditional power. Although
Brownian motion may sound complicated, the authors make the approach easy by start¬
ing with a simple example and building on it, one piece at a time, ultimately showing that
Brownian motion works for many different types of clinical trials.
The book will be very valuable to statisticians involved in clinical trials. The main body of
the chapters is accessible to anyone with knowledge of a standard mathematical statistics
text. More mathematically advanced readers will find rigorous developments in appen¬
dices at the end of chapters. Reading the book will develop insight into not only moni¬
toring, but power, survival analysis, safety, and other statistical issues germane to clinical
trials.
Michael
Statistical Association. All have spent formative years in the Biostatistics Research Branch
of the National Heart, Lung, and Blood Institute (NHLBI/NIH). While there, they were
intimately involved in the design and statistical monitoring of large-scale randomized
clinical trials, developing methodology to aid in their monitoring. For example,
developed, with
sequential designs, whose properties were further investigated by
approach used in the book was introduced in a very influential paper by
The statistical theory behind conditional power was developed by
and Halperin, and was the cornerstone for the conditional error approach to adaptive
clinical trials introduced by
adaptive methodology for clinical trials.
|
| adam_txt |
Contents
Introduction
A General Framework
2.1
Over Time
2.1.1
2.1.2
2.1.3
2.1.4
2.2
2.2.1
2.2.2
2.3
Brownian Motion
2.4
2.5
2.5.1
2.5.2
2.6
2.6.1
2.6.2
2.7
2.8
2.9
2.9.1
Standard Errors
2.9.2
2.9.3
Uncorrelated Under Ho
2.9.4
Drift: Local Alternatives
Contents
2.9.5 Basu's Theorem. 42
Power:
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.7.1
3.7.2
3.7.3
Historical Monitoring Boundaries
4.1
4.2
4.3
4.4
4.5
4.6
4.7
Integration
Spending Functions
5.1
5.1.1
5.1.2
5.2
5.3
5.4
5.4.1
5.4.2
5.4.3
Practical Survival Monitoring
6.1
6.2
6.3
6.4
6.5
a t-Test
6.6
6.7
Contents
7
7.1
7.2
7.2.1
7.2.2
7.2.3
7.3
7.4
7.5
7.6
7.7
One-Tailed Setting
8
8.1
8.2
8.2.1
8.2.2
8.3
8.4
8.5
8.5.1
Information Time
8.5.2
9
9.1
9.2
9.3
9.4
9.5
9.5.1
9.5.2
that Would Stop a Study
9.5.3
Should Report
9.6
9.7
10
10.1
10.2
10.3
10.4
10.5
XII Contents
10.6
11
11.1
11.2
Continuous Outcome Case
11.2.1
11.2.2
11.2.3
11.2.4
11.2.5
11.2.6
11.3
Outcome Case
11.3.1
11.4
11.4.1
11.4.2
11.5
12
12.1
12.2
12.3
12.4
12.5
12.6
12.7
13
13.1
13.2
13.2.1
13.2.2
13.3
13.4
13.5
13.6
13.7
14
14.1
14.2
14.3
14.3.1
Contents XIII
14.4 After
Confidence Interval
14.5
References
Index
The approach taken in this book is, to studies monitored over time, what the Central
Limit Theorem is to studies with only one analysis. Just as the Central Limit Theorem
shows that test statistics involving very different types of clinical trial outcomes are
asymptotically normal, this book shows that the joint distribution of the test statistics at
different analysis times is asymptotically multivariate normal with the correlation struc¬
ture of Brownian motion ("the B-value") irrespective of the test statistic. The so-called
B-value approach to monitoring allows us to use, for different types of trials, the same
boundaries and the same simple formula for computing conditional power. Although
Brownian motion may sound complicated, the authors make the approach easy by start¬
ing with a simple example and building on it, one piece at a time, ultimately showing that
Brownian motion works for many different types of clinical trials.
The book will be very valuable to statisticians involved in clinical trials. The main body of
the chapters is accessible to anyone with knowledge of a standard mathematical statistics
text. More mathematically advanced readers will find rigorous developments in appen¬
dices at the end of chapters. Reading the book will develop insight into not only moni¬
toring, but power, survival analysis, safety, and other statistical issues germane to clinical
trials.
Michael
Statistical Association. All have spent formative years in the Biostatistics Research Branch
of the National Heart, Lung, and Blood Institute (NHLBI/NIH). While there, they were
intimately involved in the design and statistical monitoring of large-scale randomized
clinical trials, developing methodology to aid in their monitoring. For example,
developed, with
sequential designs, whose properties were further investigated by
approach used in the book was introduced in a very influential paper by
The statistical theory behind conditional power was developed by
and Halperin, and was the cornerstone for the conditional error approach to adaptive
clinical trials introduced by
adaptive methodology for clinical trials. |
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| index_date | 2024-07-02T14:46:16Z |
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| series2 | Statistics for biology and health |
| spelling | Proschan, Michael A. Verfasser aut Statistical monitoring of clinical trials a unified approach Michael A. Proschan ; K. K. Gordon Lan ; Janet Turk Wittes New York, NY Springer 2006 XIII, 258 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Statistics for biology and health Essais cliniques comme sujet Études cliniques - Méthodes statistiques ram Statistik Bayes Theorem Clinical Trials statistics & numerical data Data Interpretation, Statistical Drugs Testing Statistics Klinisches Experiment (DE-588)4164223-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Medizinische Statistik (DE-588)4127563-9 gnd rswk-swf Datenanalyse (DE-588)4123037-1 s Medizinische Statistik (DE-588)4127563-9 s Klinisches Experiment (DE-588)4164223-5 s DE-604 Lan, K. K. Gordon Verfasser aut Wittes, Janet Turk Verfasser aut text/html http://deposit.dnb.de/cgi-bin/dokserv?id=2687849&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014811826&sequence=000003&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=014811826&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Proschan, Michael A. Lan, K. K. Gordon Wittes, Janet Turk Statistical monitoring of clinical trials a unified approach Essais cliniques comme sujet Études cliniques - Méthodes statistiques ram Statistik Bayes Theorem Clinical Trials statistics & numerical data Data Interpretation, Statistical Drugs Testing Statistics Klinisches Experiment (DE-588)4164223-5 gnd Datenanalyse (DE-588)4123037-1 gnd Medizinische Statistik (DE-588)4127563-9 gnd |
| subject_GND | (DE-588)4164223-5 (DE-588)4123037-1 (DE-588)4127563-9 |
| title | Statistical monitoring of clinical trials a unified approach |
| title_auth | Statistical monitoring of clinical trials a unified approach |
| title_exact_search | Statistical monitoring of clinical trials a unified approach |
| title_exact_search_txtP | Statistical monitoring of clinical trials a unified approach |
| title_full | Statistical monitoring of clinical trials a unified approach Michael A. Proschan ; K. K. Gordon Lan ; Janet Turk Wittes |
| title_fullStr | Statistical monitoring of clinical trials a unified approach Michael A. Proschan ; K. K. Gordon Lan ; Janet Turk Wittes |
| title_full_unstemmed | Statistical monitoring of clinical trials a unified approach Michael A. Proschan ; K. K. Gordon Lan ; Janet Turk Wittes |
| title_short | Statistical monitoring of clinical trials |
| title_sort | statistical monitoring of clinical trials a unified approach |
| title_sub | a unified approach |
| topic | Essais cliniques comme sujet Études cliniques - Méthodes statistiques ram Statistik Bayes Theorem Clinical Trials statistics & numerical data Data Interpretation, Statistical Drugs Testing Statistics Klinisches Experiment (DE-588)4164223-5 gnd Datenanalyse (DE-588)4123037-1 gnd Medizinische Statistik (DE-588)4127563-9 gnd |
| topic_facet | Essais cliniques comme sujet Études cliniques - Méthodes statistiques Statistik Bayes Theorem Clinical Trials statistics & numerical data Data Interpretation, Statistical Drugs Testing Statistics Klinisches Experiment Datenanalyse Medizinische Statistik |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=2687849&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014811826&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014811826&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT proschanmichaela statisticalmonitoringofclinicaltrialsaunifiedapproach AT lankkgordon statisticalmonitoringofclinicaltrialsaunifiedapproach AT wittesjanetturk statisticalmonitoringofclinicaltrialsaunifiedapproach |