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Power Analysis of Trials with Multilevel Data [Kietas viršelis]

3.00/5 (2 ratings by Goodreads)
(Radboud University Medical Center, The Netherlands), (Utrecht University, The Netherlands)
  • Formatas: Hardback, 288 pages, aukštis x plotis: 234x156 mm, weight: 540 g, 23 Tables, black and white; 48 Illustrations, black and white
  • Serija: Chapman & Hall/CRC Interdisciplinary Statistics
  • Išleidimo metai: 07-Jul-2015
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1498729894
  • ISBN-13: 9781498729895
Kitos knygos pagal šią temą:
Power Analysis of Trials with Multilevel DataDidesnis paveikslėlis
  • Formatas: Hardback, 288 pages, aukštis x plotis: 234x156 mm, weight: 540 g, 23 Tables, black and white; 48 Illustrations, black and white
  • Serija: Chapman & Hall/CRC Interdisciplinary Statistics
  • Išleidimo metai: 07-Jul-2015
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1498729894
  • ISBN-13: 9781498729895
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781498729895.html
Raktažodžiai:

Power Analysis of Trials with Multilevel Data covers using power and sample size calculations to design trials that involve nested data structures. The book gives a thorough overview of power analysis that details terminology and notation, outlines key concepts of statistical power and power analysis, and explains why they are necessary in trial design. It guides you in performing power calculations with hierarchical data, which enables more effective trial design.

The authors are leading experts in the field who recognize that power analysis has attracted attention from applied statisticians in social, behavioral, medical, and health science. Their book supplies formulae that allow statisticians and researchers in these fields to perform calculations that enable them to plan cost-efficient trials. The formulae can also be applied to other sciences.

Using power analysis in trial design is increasingly important in a scientific community where experimentation is often expensive, competition for funding among researchers is intense, and agencies that finance research require proposals to give thorough justification for funding. This handbook shows how power analysis shapes trial designs that have high statistical power and low cost, using real-life examples.

The book covers multiple types of trials, including cluster randomized trials, multisite trials, individually randomized group treatment trials, and longitudinal intervention studies. It also offers insight on choosing which trial is best suited to a given project. Power Analysis of Trials with Multilevel Data helps you craft an optimal research design and anticipate the necessary sample size of data to collect to give your research maximum effectiveness and efficiency.

Recenzijos

"I enjoyed reviewing the new CRC Press/Chapman Hall book entitled Power Analysis of Trials with Multilevel Data, by Mirjam Moerbeek and Steven Teerenstra. This book addresses a critical need in the scientific community for a well-organized, easily accessible guide to performing power analysis and computing required sample sizes for randomized trials embedded in multilevel study designs, where observations of interest are nested within higher level units (e.g. patients within clinics or repeated measures on participants). This book effectively compiles all the published literature on this specialized topic, putting it in one place for researchers who design these types of studies and could benefit from a concise and practical resource on this important aspect of study design. The two Dutch authors are experts in this area and are very well-equipped to provide more general education and practical advice on this topic. Multilevel study designs in which power analysis methods for independent observations do not apply are quite common, but no prior books have attempted to organize all the possible power analysis approaches for these types of studies into a single reference. In sum, this will be a very useful book for researchers, statisticians, and consultants responsible for designing various types of randomized trials in multilevel settings. My minor quibbles are far outweighed by the important contributions that this single resource on power analysis in multilevel designs will make to the scientific community." Brady T. West, University of Michigan, Biometrical Journal, May 2017

"the appearance of the book, Power Analysis of Trials with Multilevel Data, is well timedAnother nice feature of the book is the example power analyses that conclude most chapters (and sometimes appear earlier in chapters as well). The authors have done a very good job finding articles in the literature that use a particular design, extracting relevant parameters from those articles, and then illustrating how to use those parameters to plan a replication studyI think this book deserves a place on the bookshelf of both researchers who plan experimental studies and statisticians who advise them." Christopher H. Rhoads, University of Connecticut, The American Statistician, November 2016 "I enjoyed reviewing the new CRC Press/Chapman Hall book entitled Power Analysis of Trials with Multilevel Data, by Mirjam Moerbeek and Steven Teerenstra. This book addresses a critical need in the scientific community for a well-organized, easily accessible guide to performing power analysis and computing required sample sizes for randomized trials embedded in multilevel study designs, where observations of interest are nested within higher level units (e.g. patients within clinics or repeated measures on participants). This book effectively compiles all the published literature on this specialized topic, putting it in one place for researchers who design these types of studies and could benefit from a concise and practical resource on this important aspect of study design. The two Dutch authors are experts in this area and are very well-equipped to provide more general education and practical advice on this topic. Multilevel study designs in which power analysis methods for independent observations do not apply are quite common, but no prior books have attempted to organize all the possible power analysis approaches for these types of studies into a single reference. In sum, this will be a very useful book for researchers, statisticians, and consultants responsible for designing various types of randomized trials in multilevel settings. My minor quibbles are far outweighed by the important contributions that this single resource on power analysis in multilevel designs will make to the scientific community." Brady T. West, University of Michigan, Biometrical Journal, May 2017

"the appearance of the book, Power Analysis of Trials with Multilevel Data, is well timedAnother nice feature of the book is the example power analyses that conclude most chapters (and sometimes appear earlier in chapters as well). The authors have done a very good job finding articles in the literature that use a particular design, extracting relevant parameters from those articles, and then illustrating how to use those parameters to plan a replication studyI think this book deserves a place on the bookshelf of both researchers who plan experimental studies and statisticians who advise them." Christopher H. Rhoads, University of Connecticut, The American Statistician, November 2016

List of figures
xi
List of tables
xv
Preface xvii
1 Introduction
1(20)
1.1 Experimentation
2(4)
1.1.1 Problems with random assignment
5(1)
1.2 Hierarchical data structures
6(3)
1.3 Research design
9(6)
1.3.1 Cluster randomized trial
10(1)
1.3.2 Multisite trial
11(1)
1.3.3 Pseudo cluster randomized trial
12(1)
1.3.4 Individually randomized group treatment trial
12(1)
1.3.5 Longitudinal intervention study
13(1)
1.3.6 Some guidance to design choice
14(1)
1.4 Power analysis for experimental research
15(3)
1.5 Aim and contents of the book
18(3)
1.5.1 Aim
18(1)
1.5.2 Contents
18(3)
2 Multilevel statistical models
21(18)
2.1 The basic two-level model
21(5)
2.2 Estimation and hypothesis test
26(3)
2.3 Intraclass correlation coefficient
29(3)
2.4 Multilevel models for dichotomous outcomes
32(3)
2.5 More than two levels of nesting
35(2)
2.6 Software for multilevel analysis
37(2)
3 Concepts of statistical power analysis
39(24)
3.1 Background of power analysis
39(8)
3.1.1 Hypotheses testing
39(2)
3.1.2 Power calculations for continuous outcomes
41(4)
3.1.3 Power calculations for dichotomous outcomes
45(1)
3.1.3.1 Risk difference
45(1)
3.1.3.2 Odds ratio
46(1)
3.2 Types of power analysis
47(2)
3.3 Timing of power analysis
49(1)
3.4 Methods for power analysis
50(2)
3.5 Robustness of power and sample size calculations
52(1)
3.6 Procedure for a priori power analysis
53(4)
3.6.1 An example
56(1)
3.7 The optimal design of experiments
57(2)
3.7.1 An example (continued)
59(1)
3.8 Sample size and precision analysis
59(2)
3.9 Sample size and accuracy of parameter estimates
61(2)
4 Cluster randomized trials
63(20)
4.1 Introduction
63(2)
4.2 Multilevel model
65(3)
4.3 Sample size calculations for continuous outcomes
68(10)
4.3.1 Factors that influence power
69(3)
4.3.2 Design effect
72(1)
4.3.3 Sample size formulae for fixed cluster size or fixed number of clusters
73(2)
4.3.4 Including budgetary constraints
75(3)
4.4 Sample size calculations for dichotomous outcomes
78(3)
4.4.1 Risk difference
79(1)
4.4.2 Odds ratio
80(1)
4.5 An example
81(2)
5 Improving statistical power in cluster randomized trials
83(24)
5.1 Inclusion of covariates
84(3)
5.2 Minimization, matching, pre-stratification
87(3)
5.3 Taking repeated measurements
90(4)
5.4 Crossover in cluster randomized trials
94(7)
5.5 Stepped wedge designs
101(6)
6 Multisite trials
107(22)
6.1 Introduction
107(2)
6.2 Multilevel model
109(6)
6.3 Sample size calculations for continuous outcomes
115(9)
6.3.1 Factors that influence power
115(3)
6.3.2 Design effect
118(2)
6.3.3 Sample size formulae for fixed cluster size or fixed number of clusters
120(1)
6.3.4 Including budgetary constraints
121(1)
6.3.5 Constant treatment effect
122(2)
6.4 Sample size calculations for dichotomous outcomes
124(2)
6.4.1 Odds ratio
125(1)
6.5 An example
126(3)
7 Pseudo cluster randomized trials
129(12)
7.1 Introduction
129(3)
7.2 Multilevel model
132(2)
7.3 Sample size calculations for continuous outcomes
134(4)
7.3.1 Factors that influence power
134(2)
7.3.2 Design effect
136(1)
7.3.3 Sample size formulae for fixed cluster size or fixed number of clusters
137(1)
7.4 Sample size calculations for binary outcomes
138(2)
7.5 An example
140(1)
8 Individually randomized group treatment trials
141(18)
8.1 Introduction
141(2)
8.2 Multilevel model
143(3)
8.2.1 Clustering in both treatment arms
143(2)
8.2.2 Clustering in one treatment arm
145(1)
8.3 Sample size calculations for continuous outcomes
146(7)
8.3.1 Clustering in both treatment arms
146(1)
8.3.1.1 Factors that influence power
146(1)
8.3.1.2 Sample size formulae for fixed cluster sizes
147(1)
8.3.1.3 Including budgetary constraints
148(2)
8.3.2 Clustering in one treatment arm
150(1)
8.3.2.1 Factors that influence power
150(1)
8.3.2.2 Sample size formulae for fixed cluster sizes
151(1)
8.3.2.3 Including budgetary constraints
151(2)
8.4 Sample size calculations for dichotomous outcomes
153(2)
8.4.1 Clustering in both treatment arms
153(1)
8.4.2 Clustering in one treatment arm
154(1)
8.5 An example
155(4)
9 Longitudinal intervention studies
159(24)
9.1 Introduction
159(2)
9.2 Multilevel model
161(4)
9.3 Sample size calculations for continuous outcomes
165(5)
9.3.1 Factors that influence power
165(3)
9.3.2 Sample size formula for fixed number of measurements
168(1)
9.3.3 Including budgetary constraints
169(1)
9.4 Sample size calculations for dichotomous outcomes
170(2)
9.4.1 Odds ratio
171(1)
9.5 The effect of drop-out on statistical power
172(8)
9.5.1 The effects of different drop-out patterns
173(6)
9.5.2 Including budgetary constraints
179(1)
9.6 An example
180(3)
10 Extensions: three levels of nesting and factorial designs
183(20)
10.1 Introduction
183(1)
10.2 Three-level cluster randomized trials
184(4)
10.3 Multisite cluster randomized trials
188(5)
10.4 Repeated measures in cluster randomized trials and multisite trials
193(5)
10.5 Factorial designs
198(5)
10.5.1 Continuous outcome
198(1)
10.5.2 Binary outcome
199(1)
10.5.3 Sample size calculation for factorial designs
200(3)
11 The problem of unknown intraclass correlation coefficients
203(14)
11.1 Estimates from previous research
204(1)
11.2 Sample size re-estimation
205(6)
11.3 Bayesian sample size calculation
211(3)
11.4 Maximin optimal designs
214(3)
12 Computer software for power calculations
217(12)
12.1 Introduction
217(1)
12.2 Computer program SPA-ML
218(11)
References 229(26)
Author Index 255(10)
Subject Index 265
Mirjam Moerbeek is an associate professor at Utrecht University, the Netherlands. She obtained her masters degree (cum laude) in biometrics from Wageningen Agricultural University in 1996 and her PhD in applied statistics from Maastricht University in 2000. She has received prestigious research grants from the Netherlands Organisation for Scientific Research (NWO) as well as grants to hire PhD students. Her research interests are statistical power analysis and optimal experimental design, especially for hierarchical and survival data. She was involved in organizing a colloquium and class on cost-efficient and optimal designs for the Royal Netherlands Academy of Arts and Sciences (KNAW) and is a joint organizer of the biennial International Conference on Multilevel Analysis.

Steven Teerenstra received his MSc and PhD in mathematics at Radboud University in 1996 and 2004, respectively, as well as his MSc in theoretical physics in 2006. He is currently a biostatistician at Radboud University Nijmegen Medical Center, involved in research, consultation and conduct of cluster randomized trials. He is appointed assessor of statistics and methodology at the Dutch Medicines Evaluation Board and a member of the Biostatistics Working Party at the European Medicines Agency.