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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Campbell, Michael J.
Otros Autores: Walters, Stephen J.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken : Wiley, 2014.
Colección:Statistics in practice.
Temas:
Randomized Controlled Trials as Topic.
Data Interpretation, Statistical.
Health Services Research > method.
Research Design.
Randomized Controlled Trials as Topic
Data Interpretation, Statistical
Health Services Research > methods
Research Design
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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 u 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
  • 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.

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