How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research

Content: Statistics in Practice
 Title Page
 Copyright
 Preface
 Acronyms and abbreviations
 Chapter 1: Introduction
 1.1 Randomised controlled trials
 1.2 Complex interventions
 1.3 History of cluster randomised trials
 1.4 Cohort and field trials
 1.5 The field/community trial
 1.6 The cohort trial
 1.7 Field versus cohort designs
 1.8 Reasons for cluster trials
 1.9 Between- and within-cluster variation
 1.10 Random-effects models for continuous outcomes
 1.11 Random-effects models for binary outcomes
 1.12 The design effect
 1.13 Commonly asked questions
 1.14 Websources
 Chapter 2: Design issues 2.1 Introduction2.2 Issues for a simple intervention
 2.3 Complex interventions
 2.4 Recruitment bias
 2.5 Matched-pair trials
 2.6 Other types of designs
 2.7 Other design issues
 2.8 Strategies for improving precision
 2.9 Randomisation
 Exercise
 Appendix 2.A
 Chapter 3: Sample size: How many subjects/clusters do I need for my cluster randomised controlled trial?
 3.1 Introduction
 3.2 Sample size for continuous data --
comparing two means
 3.3 Sample size for binary data --
comparing two proportions
 3.4 Sample size for ordered categorical (ordinal) data
 3.5 Sample size for rates 3.6 Sample size for survival3.7 Equivalence/non-inferiority studies
 3.8 Unknown standard deviation and effect size
 3.9 Practical problems
 3.10 Number of clusters fixed
 3.11 Values of the ICC
 3.12 Allowing for imprecision in the ICC
 3.13 Allowing for varying cluster sizes
 3.14 Sample size re-estimation
 3.15 Matched-pair studies
 3.16 Multiple outcomes/endpoints
 3.17 Three or more groups
 3.18 Crossover trials
 3.19 Post hoc sample size calculations
 3.20 Conclusion: Usefulness of sample size calculations
 3.21 Commonly asked questions
 Exercise
 Appendix 3.A Chapter 4: Simple analysis of cRCT outcomes using aggregate cluster-level summaries4.1 Introduction
 4.2 Aggregate cluster-level analysis-carried out at the cluster level, using aggregate summary data
 4.3 Statistical methods for continuous outcomes
 4.4 Mann-Whitney U test
 4.5 Statistical methods for binary outcomes
 4.6 Analysis of a matched design
 4.7 Discussion
 4.8 Commonly asked question
 Exercise
 Chapter 5: Regression methods of analysis for continuous outcomes using individual person-level data
 5.1 Introduction
 5.2 Incorrect models 5.3 Linear regression with robust standard errors5.4 Random-effects general linear models in a cohort study
 5.5 Marginal general linear model with coefficients estimated by generalised estimating equations (GEE)
 5.6 Summary of methods
 5.7 Adjusting for individual-level covariates in cohort studies
 5.8 Adjusting for cluster-level covariates in cohort studies
 5.9 Models for cross-sectional designs
 5.10 Discussion of model fitting
 Exercise
 Appendix 5.A
 Chapter 6: Regression methods of analysis for binary, count and time-to-event outcomes for a cluster randomised controlled trial