Main description:

There is an increasing need for new health interventions to be rigorously evaluated through randomized controlled trials, which have become the gold standard for intervention studies. In addition, there is increasing acceptance of cluster randomized trials (CRTs) as the most appropriate choice of study design. "Cluster Randomised Trials" covers the design, analysis, and conduct of health intervention trails in which groups or clusters of individuals are randomized to different conditions. With an emphasis on key concepts rather than mathematical details, the book uses case studies to illustrate methods and Stata code based on real-world datasets to demonstrate analytical methods.

Contents:

BASIC CONCEPTS Introduction Randomised Trials Variability between Clusters Introduction The Implications of between-Cluster Variability: Some Examples Measures of between-Cluster Variability The Design Effect Sources of within-Cluster Correlation Choosing whether to Randomise by Cluster Introduction Rationale for Cluster Randomisation Using Cluster Randomisation to Capture Indirect Effects of Intervention Disadvantages and Limitations of Cluster Randomisation DESIGN ISSUES Choice of Clusters Introduction Types of Cluster Size of Clusters Strategies to Reduce Contamination Levels of Randomisation, Intervention, Data Collection, and Inference Matching and Stratification Introduction Rationale for Matching Disadvantages of Matching Stratification as an Alternative to Matching Choice of Matching Variables Choosing whether to Match or Stratify Randomisation Procedures Introduction Restricted Randomisation Some Practical Aspects of Randomisation Sample Size Introduction Sample Size for Unmatched Trials Sample Size for Matched and Stratified Trials Estimating the between-Cluster Coefficient of Variation Choice of Sample Size in Each Cluster Further Issues in Sample Size Calculation Alternative Study Designs Introduction Design Choices for Treatment Arms Design Choices for Impact Evaluation ANALYTICAL METHODS Basic Principles of Analysis Introduction Experimental and Observational Units Parameters of Interest Approaches to Analysis Baseline Analysis Analysis Based on Cluster-Level Summaries Introduction Point Estimates of Intervention Effects Statistical Inference Based on the t Distribution Statistical Inference Based on a Quasilikelihood Approach Adjusting for Covariates Nonparametric Methods Analysing for Effect Modification Regression Analysis Based on Individual-Level Data Introduction Random Effects Models Generalised Estimating Equations Choice of Analytical Method Analysing for Effect Modification More Complex Analyses Analysis of Trials with More Complex Designs Introduction Analysis of Pair-Matched Trials Analysis of Stratified Trials Analysis of Other Study Designs MISCELLANEOUS TOPICS Ethical Considerations Introduction General Principles Ethical Issues in Group Allocation Informed Consent in Cluster Randomised Trials Other Ethical Issues Conclusion Data Monitoring Introduction Data Monitoring Committees Interim Analyses Reporting and Interpretation Introduction Reporting of Cluster Randomised Trials Interpretation and Generalisability References Index