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"A much-needed guide to the design and analysis of cluster randomized trials, How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research delivers practical guidance on the design and analysis of cluster randomisedtrials (cRCTs) in healthcare research. Detailing how to use Stata and SPSS and R for statistical analysis, each analysis technique is carefully explained with mathematics kept to a minimum. Written in a clear, accessible style by experienced statisticians, the text provides a practical approach for applied statisticians and biomedical researchers"--Provided by publisher.
Inhaltsverzeichnis
Preface xiii
Acronyms and abbreviations xv
1 Introduction 1
1.1 Randomised controlled trials 1
1.1.1 A-Allocation at random 1
1.1.2 B-Blindness 2
1.1.3 C-Control 2
1.2 Complex interventions 3
1.3 History of cluster randomised trials 4
1.4 Cohort and field trials 4
1.5 The field/community trial 5
1.5.1 The REACT trial 5
1.5.2 The Informed Choice leaflets trial 6
1.5.3 The Mwanza trial 7
1.5.4 The paramedics practitioner trial 7
1.6 The cohort trial 8
1.6.1 The PoNDER trial 8
1.6.2 The DESMOND trial 9
1.6.3 The Diabetes Care from Diagnosis trial 10
1.6.4 The REPOSE trial 11
1.6.5 Other examples of cohort cluster trials 11
1.7 Field versus cohort designs 11
1.8 Reasons for cluster trials 12
1.9 Between- and within-cluster variation 14
1.10 Random-effects models for continuous outcomes 15
1.10.1 The model 15
1.10.2 The intracluster correlation coefficient 16
1.10.3 Estimating the intracluster correlation (ICC) coefficient 16
1.10.4 Link between the Pearson correlation coefficient and the intraclass correlation coefficient 17
1.11 Random-effects models for binary outcomes 18
1.11.1 The model 18
1.11.2 The ICC for binary data 19
1.11.3 The coefficient of variation 19
1.11.4 Relationship between cvc and for binary data 20
1.12 The design effect 20
1.13 Commonly asked questions 21
1.14 Websources 21
Exercise 22
Appendix 1.A 22
2 Design issues 27
2.1 Introduction 27
2.2 Issues for a simple intervention 28
2.2.1 Phases of a trial 28
2.2.2 'Pragmatic' and 'explanatory' trials 29
2.2.3 Intention-to-treat and per-protocol analyses 29
2.2.4 Non-inferiority and equivalence trials 30
2.3 Complex interventions 30
2.3.1 Design of complex interventions 30
2.3.2 Phase I modelling/qualitative designs 32
2.3.3 Pilot or feasibility studies 33
2.3.4 Example of pilot/feasibility studies in cluster trials 33
2.4 Recruitment bias 34
2.5 Matched-pair trials 34
2.5.1 Design of matched-pair studies 34
2.5.2 Limitations of matched-pairs designs 36
2.5.3 Example of matched-pair design: The Family Heart Study 36
2.6 Other types of designs 37
2.6.1 Cluster factorial designs 37
2.6.2 Example cluster factorial trial 38
2.6.3 Cluster crossover trials 38
2.6.4 Example of a cluster crossover trial 39
2.6.5 Stepped wedge 39
2.6.6 Pseudorandomised trials 40
2.7 Other design issues 41
2.8 Strategies for improving precision 41
2.9 Randomisation 42
2.9.1 Reasons for randomisation 42
2.9.2 Simple randomisation 43
2.9.3 Stratified randomisation 43
2.9.4 Restricted randomisation 43
2.9.5 Minimisation 44
Exercise 45
Appendix 2.A 48
3 Sample size: How many subjects/clusters do I need for my cluster randomised controlled trial? 50
3.1 Introduction 51
3.1.1 Justification of the requirement for a sample size 51
3.1.2 Significance tests, P-values and power 51
3.1.3 Sample size and cluster trials 53
3.2 Sample size for continuous data - comparing two means 53
3.2.1 Basic formulae 53
3.2.2 The design effect (DE) in cluster RCTs 54
3.2.3 Exam
Über den Autor / die Autorin
MICHAEL J. CAMPBELL and
STEPHEN J. WALTERS,
Medical Statistics Group, School of Health and Related Research, University of Sheffield, UK
Zusammenfassung
A much-needed guide to the design and analysis of cluster randomized trials, How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research delivers practical guidance on the design and analysis of cluster randomised trials (cRCTs) in healthcare research.
