How to design, analyse and report cluster randomised trials in medicine and health related research

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...

Full description

Saved in:
Bibliographic Details
Main Authors Campbell, Michael J, Walters, Stephen J
Format eBook Book
LanguageEnglish
Published Chichester, U.K WILEY 2014
Wiley
John Wiley & Sons, Incorporated
Wiley-Blackwell
Edition1
SeriesStatistics in practice
Subjects
Clinical Medicine
Clinical trials
Clinical trials > Design
Clinical trials > Reporting
Clinical trials > Statistical methods
Data Interpretation, Statistical
MEDICAL
Online AccessGet full text
ISBN1118763599
9781118763599
9781119992028
1119992028
9781118763605
1118763602
DOI10.1002/9781118763452

Cover

Table of Contents:
  • How to design, analyse and report cluster randomised trials in medicine and health related research -- Contents -- Preface -- Acronyms and abbreviations -- 1. Introduction -- 2. Design issues -- 3. Sample size: How many subjects/clusters do I need for my cluster randomised controlled trial? -- 4. Simple analysis of cRCT outcomes using aggregate cluster-level summaries -- 5. Regression methods of analysis for continuous outcomes using individual person-level data -- 6. Regression methods of analysis for binary, count and time-to-event outcomes for a cluster randomised controlled trial -- 7. The protocol -- 8. Reporting of cRCTs -- 9. Practical issues -- 10. Computing software -- References -- Index -- Statistics in Practice.
  • 4.8 Commonly asked question -- Exercise -- Appendix 4.A -- Chapter 5 Regression methods of analysis for continuous outcomes using individual person-level data -- 5.1 Introduction -- 5.2 Incorrect models -- 5.2.1 The simple (independence) model -- 5.2.2 Fixed effects -- 5.3 Linear regression with robust standard errors -- 5.3.1 Robust standard errors -- 5.3.2 Example of use of robust standard errors -- 5.3.3 Cluster-specific versus population-averaged models -- 5.4 Random-effects general linear models in a cohort study -- 5.4.1 General models -- 5.4.2 Fitting a random-effects model -- 5.4.3 Example of a random-effects model from the PoNDER study -- 5.4.4 Checking the assumptions -- 5.5 Marginal general linear model with coefficients estimated by generalised estimating equations (GEE) -- 5.5.1 Generalised estimating equations -- 5.5.2 Example of a marginal model from the PoNDER study -- 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 -- 6.1 Introduction -- 6.2 Difference between a cluster-specific model and a population-averaged or marginal model for binary data -- 6.3 Analysis of binary data using logistic regression -- 6.4 Review of past simulations to determine efficiency of different methods for binary data -- 6.5 Analysis using summary measures -- 6.6 Analysis using logistic regression (ignoring clustering) -- 6.7 Random-effects logistic regression -- 6.8 Marginal models using generalised estimating equations -- 6.9 Analysis of count data -- 6.10 Survival analysis with cluster trials -- 6.11 Missing data
  • 2.3.3 Pilot or feasibility studies -- 2.3.4 Example of pilot/feasibility studies in cluster trials -- 2.4 Recruitment bias -- 2.5 Matched-pair trials -- 2.5.1 Design of matched-pair studies -- 2.5.2 Limitations of matched-pairs designs -- 2.5.3 Example of matched-pair design: The Family Heart Study -- 2.6 Other types of designs -- 2.6.1 Cluster factorial designs -- 2.6.2 Example cluster factorial trial -- 2.6.3 Cluster crossover trials -- 2.6.4 Example of a cluster crossover trial -- 2.6.5 Stepped wedge -- 2.6.6 Pseudorandomised trials -- 2.7 Other design issues -- 2.8 Strategies for improving precision -- 2.9 Randomisation -- 2.9.1 Reasons for randomisation -- 2.9.2 Simple randomisation -- 2.9.3 Stratified randomisation -- 2.9.4 Restricted randomisation -- 2.9.5 Minimisation -- Exercise -- Appendix 2.A -- Chapter 3 Sample size: How many subjects/clusters do I need for my cluster randomised controlled trial? -- 3.1 Introduction -- 3.1.1 Justification of the requirement for a sample size -- 3.1.2 Significance tests, P-values and power -- 3.1.3 Sample size and cluster trials -- 3.2 Sample size for continuous data-comparing two means -- 3.2.1 Basic formulae -- 3.2.2 The design effect (DE) in cluster RCTs -- 3.2.3 Example from general practice -- 3.3 Sample size for binary data-comparing two proportions -- 3.3.1 Sample size formula -- 3.3.2 Example calculations -- 3.3.3 Example: The Informed Choice leaflets study -- 3.4 Sample size for ordered categorical (ordinal) data -- 3.4.1 Sample size formula -- 3.4.2 Example calculations -- 3.5 Sample size for rates -- 3.5.1 Formulae -- 3.5.2 Example comparing rates -- 3.6 Sample size for survival -- 3.6.1 Formulae -- 3.6.2 Example of sample size for survival -- 3.7 Equivalence/non-inferiority studies -- 3.7.1 Equivalence/non-inferiority versus superiority
  • 6.12 Discussion -- Exercise -- Appendix 6.A -- Chapter 7 The protocol -- 7.1 Introduction -- 7.2 Abstract -- 7.3 Protocol background -- 7.4 Research objectives -- 7.5 Outcome measures -- 7.6 Design -- 7.7 Intervention details -- 7.8 Eligibility -- 7.9 Randomisation -- 7.10 Assessment and data collection -- 7.11 Statistical considerations -- 7.11.1 Sample size -- 7.11.2 Statistical analysis -- 7.11.3 Interim analyses -- 7.12 Ethics -- 7.12.1 Declaration of Helsinki -- 7.12.2 Informed consent -- 7.13 Organisation -- 7.13.1 The team -- 7.13.2 Trial forms -- 7.13.3 Data management -- 7.13.4 Protocol amendments -- 7.14 Further reading -- Exercise -- Chapter 8 Reporting of cRCTs -- 8.1 Introduction: Extended CONSORT guidelines for reporting and presenting the results from cRCTs -- 8.2 Patient flow diagram -- 8.3 Comparison of entry characteristics -- 8.4 Incomplete data -- 8.5 Reporting the main outcome -- 8.6 Subgroup analysis and analysis of secondary outcomes/endpoints -- 8.7 Estimates of between-cluster variability -- 8.7.1 Example of reporting the ICC: The PoNDER cRCT -- 8.8 Further reading -- Exercise -- Chapter 9 Practical issues -- 9.1 Preventing bias in cluster randomised controlled trials -- 9.1.1 Problems with identifying and recruiting patients to cluster trials -- 9.1.2 Preventing biased recruitment -- 9.1.2.1 Use individual random allocation to treatment groups -- 9.1.2.2 Prior identification of participants before random allocation of the clusters -- 9.1.2.3 Independent recruitment of participants to clusters -- 9.2 Developing complex interventions -- 9.3 Choice of method of analysis -- 9.4 Missing data -- 9.5 Example sensitivity analysis: Imputation of missing 6-month EPDS data for at-risk women from the PoNDER cRCT -- 9.6 Multiplicity of outcomes -- 9.6.1 Limiting the number of confirmatory tests -- 9.6.2 Summary measures and statistics
  • 3.7.2 Continuous data-comparing the equivalence of two means -- 3.7.3 Example calculations for continuous data -- 3.7.4 Binary data-comparing the equivalence of two proportions -- 3.8 Unknown standard deviation and effect size -- 3.9 Practical problems -- 3.9.1 Tips on getting the SD -- 3.9.2 Non-response -- 3.9.3 Unequal groups -- 3.10 Number of clusters fixed -- 3.10.1 Number of clusters and number of subjects per cluster -- 3.10.2 Example with number of clusters fixed -- 3.10.3 Increasing the number of clusters or number of patients per cluster? -- 3.11 Values of the ICC -- 3.12 Allowing for imprecision in the ICC -- 3.13 Allowing for varying cluster sizes -- 3.13.1 Formulae -- 3.13.2 Example of effect of variable cluster size -- 3.14 Sample size re-estimation -- 3.14.1 Adjusting for covariates -- 3.15 Matched-pair studies -- 3.15.1 Sample sizes for matched designs -- 3.15.2 Example of a sample size calculation for a matched study -- 3.16 Multiple outcomes/endpoints -- 3.17 Three or more groups -- 3.18 Crossover trials -- 3.18.1 Formulae -- 3.18.2 Example of a sample size formula in a crossover trial -- 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 summaries -- 4.1 Introduction -- 4.1.1 Methods of analysing cluster randomised trials -- 4.1.2 Choosing the statistical method -- 4.2 Aggregate cluster-level analysis-carried out at the cluster level, using aggregate summary data -- 4.3 Statistical methods for continuous outcomes -- 4.3.1 Two independent-samples t-test -- 4.3.2 Example -- 4.3.2.1 Weighted two-sample t-test -- 4.4 Mann-Whitney U test -- 4.5 Statistical methods for binary outcomes -- 4.6 Analysis of a matched design -- 4.7 Discussion
  • Cover -- Title Page -- Copyright -- Contents -- Preface -- Acronyms and abbreviations -- Chapter 1 Introduction -- 1.1 Randomised controlled trials -- 1.1.1 A-Allocation at random -- 1.1.2 B-Blindness -- 1.1.3 C-Control -- 1.2 Complex interventions -- 1.3 History of cluster randomised trials -- 1.4 Cohort and field trials -- 1.5 The field/community trial -- 1.5.1 The REACT trial -- 1.5.2 The Informed Choice leaflets trial -- 1.5.3 The Mwanza trial -- 1.5.4 The paramedics practitioner trial -- 1.6 The cohort trial -- 1.6.1 The PoNDER trial -- 1.6.2 The DESMOND trial -- 1.6.3 The Diabetes Care from Diagnosis trial -- 1.6.4 The REPOSE trial -- 1.6.5 Other examples of cohort cluster trials -- 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 -- 1.10.1 The model -- 1.10.2 The intracluster correlation coefficient -- 1.10.3 Estimating the intracluster correlation (ICC) coefficient -- 1.10.4 Link between the Pearson correlation coefficient and the intraclass correlation coefficient -- 1.11 Random-effects models for binary outcomes -- 1.11.1 The model -- 1.11.2 The ICC for binary data -- 1.11.3 The coefficient of variation -- 1.11.4 Relationship between cvc and ρ for binary data -- 1.12 The design effect -- 1.13 Commonly asked questions -- 1.14 Websources -- Exercise -- Appendix 1.A -- Chapter 2 Design issues -- 2.1 Introduction -- 2.2 Issues for a simple intervention -- 2.2.1 Phases of a trial -- 2.2.1.1 Preclinical -- 2.2.1.2 Sequence of phases -- 2.2.2 'Pragmatic' and 'explanatory' trials -- 2.2.3 Intention-to-treat and per-protocol analyses -- 2.2.4 Non-inferiority and equivalence trials -- 2.3 Complex interventions -- 2.3.1 Design of complex interventions -- 2.3.1.1 Theory (preclinical) -- 2.3.2 Phase I modelling/qualitative designs
  • 9.6.3 Global tests and multiple comparison procedures