How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research (Statistics in Practice)

"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. 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. � Read more... Abstract: 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. � Read more... 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

A complete guide to understanding cluster randomised trials

Written by two researchers with extensive experience in the field, this book presents a complete guide to the design, analysis and reporting of cluster randomised trials. It spans a wide range of applications: trials in developing countries, trials in primary care, trials in the health services. A key feature is the use of R code and code from other popular packages to plan and analyse cluster trials, using data from actual trials. The book contains clear technical descriptions of the models used, and considers in detail the ethics involved in such trials and the problems in planning them. For readers and students who do not intend to run a trial but wish to be a critical reader of the literature, there are sections on the CONSORT statement, and exercises in reading published trials.

• Written in a clear, accessible style
• Features real examples taken from the authors' extensive practitioner experience of designing and analysing clinical trials
• Demonstrates the use of R, Stata and SPSS for statistical analysis
• Includes computer code so the reader can replicate all the analyses
• Discusses neglected areas such as ethics and practical issues in running cluster randomised trials

How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research provides an excellent reference tool and can be read with profit by statisticians, health services researchers, systematic reviewers and critical readers of cluster randomised trials.

A complete guide to understanding cluster randomised trials Written by two researchers with extensive experience in the field, this book presents a complete guide to the design, analysis and reporting of cluster randomised trials. It spans a wide range of applications: trials in developing countries, trials in primary care, trials in the health services. A key feature is the use of R code and code from other popular packages to plan and analyse cluster trials, using data from actual trials. The book contains clear technical descriptions of the models used, and considers in detail the ethics involved in such trials and the problems in planning them. For readers and students who do not intend to run a trial but wish to be a critical reader of the literature, there are sections on the CONSORT statement, and exercises in reading published trials. Written in a clear, accessible style Features real examples taken from the authors' extensive practitioner experience of designing and analysing clinical trials Demonstrates the use of R, Stata and SPSS for statistical analysis Includes computer code so the reader can replicate all the analyses Discusses neglected areas such as ethics and practical issues in running cluster randomised trials How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research provides an excellent reference tool and can be read with profit by statisticians, health services researchers, systematic reviewers and critical readers of cluster randomised trials.