A Practical Guide to Cluster Randomised Trials in Health Services Research E-Book

Sandra Eldridge

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Statistics in Practice
• Sprache: Englisch

Seitenzahl: 576

Veröffentlichungsjahr: 2012

Beschreibung

Cluster randomised trials are trials in which groups (or clusters) of individuals are randomly allocated to different forms of treatment. In health care, these trials often compare different ways of managing a disease or promoting healthy living, in contrast to conventional randomised trials which randomise individuals to different treatments, classically comparing new drugs with a placebo. They are increasingly common in health services research. This book addresses the statistical, practical, and ethical issues arising from allocating groups of individuals, or clusters, to different interventions.

 Key features: 

• Guides readers through the stages of conducting a trial, from recruitment to reporting.
• Presents a wide range of examples with particular emphasis on trials in health services research and primary care, with both principles and techniques explained.
• Topics are specifically presented in the order in which investigators think about issues when they are designing a trial.
• Combines information on the latest developments in the field together with a practical guide to the design and implementation of cluster randomised trials.
• Explains principles and techniques through numerous examples including many from the authors own experience.
• Includes a wide range of references for those who wish to read further. 

This book is intended as a practical guide, written for researchers from the health professions including doctors, psychologists, and allied health professionals, as well as statisticians involved in the design, execution, analysis and reporting of cluster randomised trials. Those with a more general interest will find the plentiful examples illuminating.

Leseprobe

Table of Contents

Cover

Statistics in Practice

Title page

Copyright page

Preface

Notation

Table of cases: Trials used as examples in more than one chapter in the book

1 Introduction

1.1 Introduction to randomised trials

1.2 Explanatory or pragmatic trials

1.3 How does a cluster randomised trial differ from other trials?

1.4 Between-cluster variability

1.5 Why carry out cluster randomised trials?

1.6 Quality of evidence from cluster randomised trials

1.7 Historical perspectives

1.8 Summary

2 Recruitment and ethics

2.1 Selecting clusters and participants to enhance external validity

2.2 Ethics of cluster randomised trials

2.3 Selection and recruitment of participants to enhance internal validity

2.4 Retention of participants in the trial

2.5 Summary

3 Designing interventions

3.1 Lack of effectiveness of interventions evaluated in cluster randomised trials

3.2 What is a complex intervention?

3.3 Phases in the development of a complex intervention

3.4 Identifying evidence for potential intervention effect (pre-clinical phase)

3.5 Understanding more about intervention components (modelling phase)

3.6 Developing the optimum intervention and study design (exploratory trial phase)

3.7 What is the intervention?

3.8 Summary

4 Pilot and feasibility studies

4.1 What is a pilot study?

4.2 Reasons for conducting pilot and feasibility studies

4.3 Designing a pilot or feasibility study

4.4 Reporting and interpreting pilot studies

4.5 Summary

5 Design

5.1 Parallel designs with only two arms

5.2 Cohort versus cross-sectional designs

5.3 Parallel designs with more than two arms

5.4 Crossover designs

5.5 Further design considerations

5.6 Summary

6 Analysis

6.1 Data collection and management

6.2 Analysis – an introduction

6.3 Analyses for two-arm, completely randomised, stratified or minimised designs

6.4 Analyses for other designs

6.5 Intention to treat and missing values

6.6 Analysis planning

6.7 Summary

7 Sample size calculations

7.1 Factors affecting sample size for cluster randomised designs

7.2 Calculating sample size using the intra-cluster correlation coefficient

7.3 Sample size calculations for rates

7.4 Restricted number of clusters

7.5 Trials with a small number of clusters

7.6 Variability in cluster size

7.7 Comparison of different measures of between-cluster variability

7.8 Matched and stratified designs

7.9 Sample size for other designs

7.10 Summary

8 The intra-cluster correlation coefficient

8.1 What is the ICC?

8.2 Sources of ICC estimates

8.3 Choosing the ICC for use in sample size calculations

8.4 Calculating ICC values

8.5 Uncertainty in ICCs

8.6 Summary

9 Other topics

9.1 Systematic reviews

9.2 Cost effectiveness analyses (by Richard Grieve)

9.3 Process evaluation

9.4 Monitoring

9.5 Summary

10 Trial reporting

10.1 Trial quality and reporting quality

10.2 Steps to improve trial reporting in the early stages of the trial

10.3 Reporting randomised trials in journal and conference abstracts

10.4 Application of CONSORT statement to cluster randomised trials

10.5 Summary

Index

Statistics in Practice

Series Advisors

Human and Biological Sciences

Stephen Senn

CRP-Santé, Luxembourg

Earth and Environmental Sciences

Marian Scott

University of Glasgow, UK

Industry, Commerce and Finance

Wolfgang Jank

University of Maryland, USA

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This edition first published 2012

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Library of Congress Cataloging-in-Publication Data

Eldridge, Sandra.

 A practical guide to cluster randomised trials in health services research / Sandra Eldridge and Sally Kerry.

p. ; cm. – (Statistics in practice)

 Includes bibliographical references and index.

 Summary: “This book aims to provide that much needed practical guide to the design, execution and analysis of cluster randomized trials in health services research”–Provided by publisher.

 ISBN 978-0-470-51047-6 (hardback) – ISBN 978-1-119-96625-8 (ePDF) – ISBN 978-1-119-96624-1 (oBook) – ISBN 978-1-119-96672-2 (ePub) – ISBN 978-1-119-96673-9 (mobi)

 I. Kerry, Sally M. II. Title. III. Series: Statistics in practice.

 [DNLM: 1. Health Services Research. 2. Randomized Controlled Trials as Topic. 3. Cluster Analysis. W 84.3]

 362.10972–dc23

2011037453

A catalogue record for this book is available from the British Library.

ISBN: 978-0-470-51047-6

Preface

Cluster randomised trials differ from the more usual sorts of randomised trials in which individuals are allocated to different trial arms. In cluster randomised trials, it is not individuals, but rather groups (or clusters) of individuals that are randomised. These groups are most usually intact social organisations such as general practices, hospitals, schools or clinics; or geographical areas such as towns or communities. Several books have already been written that bring together some of the methodological literature in this field.

We wrote this book because we felt there was need for one aimed specifically at those working in health services research, not just statisticians, but other trial investigators and those with an interest in the methodological aspects of these trials. Cluster randomised trials have become increasingly common in health services research partly because, when conducted well, they are ideally suited to addressing issues related to policy, practice and organisation of health care. In the past, however, many have failed to produce robust evidence owing to problems with their design, conduct and analysis that could have been avoided with better planning. At a time when the organisation of health care is being re-considered in many countries, high quality research in this area is vital, and our hope is that this book can contribute towards facilitating this research.

In order to make the book accessible to a wide audience, we have introduced and developed the principles underlying good practice in cluster randomised trials through a large number of examples. All of them contain useful features; some, such as COMMIT, are well known as seminal trials which have had considerable influence on the field. Many of the examples are drawn from our own involvement in empirical trials over the past 15 to 20 years, mostly in the UK primary health care setting. We hope that this book will be comprehensible to any researcher with a sound grasp of the basic principles of design and analysis of standard randomised controlled trials. In addition, we have provided an extensive range of references for those who wish to grapple with a more mathematical treatment of some of the book’s content.

The book follows, more or less, the order in which investigators might think about the stages of a trial, beginning with recruitment and ethics and ending with reporting. In between we cover the issues that are ubiquitous in books on randomised trials such as design, analysis and sample size calculation. However, we also cover issues which are common in cluster randomised trials in health services research but which have not been covered in detail in other books. These include the potential for bias when recruiting individual participants, problems with estimation of the intra-cluster correlation coefficient and its application in sample size calculations, how to include these trials in systematic reviews, cost effectiveness analysis, and process evaluation. In addition, it is now well recognised that interventions evaluated in health services research need to be thoughtfully and carefully designed and that, because of the organisational complexity of trials evaluating these interventions, a pilot or feasibility study is essential; two chapters provide guidance for those designing interventions and conducting pilot and feasibility studies. Finally, we end the book with a comprehensive elaboration of the use of the CONSORT statement in relation to cluster randomised trials. This statement has become the cornerstone of good reporting of randomised controlled trials. The main statement, updated in 2010, provides the structure for our final chapter, in which we have also incorporated guidance from the extended statements for cluster randomised, pragmatic and non-pharmacological trials.

We are aware of several areas of ongoing research related to cluster randomised trials which would have had a greater influence on the book’s contents if we were writing at a later date. For example, a group conducting research on the ethics of cluster randomised trials expect to publish guidelines in 2012. For obvious reasons, we were not able to include these in the book, but refer interested readers to the group’s website (http://crtethics.wikispaces.com).

Health economics methods relevant to cluster randomised trials are also currently developing at a rapid pace but neither of us is a health economist. We therefore asked Richard Grieve, from the London School of Hygiene and Tropical Medicine, to write the section in chapter 9 about cost effectiveness. Richard is at the forefront of developments in this area, and we are grateful to him for being willing to contribute and for providing an excellent summary of a fast moving field within the number of pages and in the style that we specified!

Finally, we need to thank other people who have contributed to this book. First and foremost our thanks must go to Lynette Edwards who proof read, not once, but several times, correcting typing and grammatical errors, and inconsistencies; any that remain are entirely our responsibility. We would also like to thank Stephen Bremner, Richard Hooper, Nadine Koehler, Clare Rutterford, Karla Diaz Ordaz, Angela Devine, and Obi Ukoumunne for reading and commenting on draft chapters and for assistance with the index; and our editors at Wiley for efficiency, patience and sound advice. We are indebted to all those who have carried out cluster randomised trials that we were able to use as examples – without them there would be no book; and amongst them we are particularly grateful to Gene Feder, Martin Underwood, Chris Griffiths, Pippa Oakeshott, Tony Kendrick, Franco Cappuccio and Jackie Sturt, whose trials appear in many places in the book, and to Gene and Martin for reading and commenting on various sections. Working with Gene and Chris kindled Sandra’s interest in this area and working with Tony and Pippa kindled Sally’s. We would not, however, have developed an enduring interest in this field without discussion with and encouragement from Deborah Ashby and Martin Bland. Finally, thanks to Dave and Graham for being longsuffering and supportive over the past year and to Louise for feeding Sandra chocolate while she struggled to meet a publisher’s deadline.

Sandra Eldridge

Sally Kerry

Notation

Subscripts

i   Represents clusters j   Represents individuals 1   Represents intervention arm 2   Represents control arm 

Frequently used notation

k   Number of clusters in each arm N   Sample size (total number of individuals analysed or number of person years) for individually randomised trial Nc   Sample size (total number of individuals analysed or number of person years) for cluster randomised trial m   Cluster size (numbers analysed) if cluster sizes are fixed mi   Number of individuals in the ith cluster    Mean cluster size ρ   Intra-cluster correlation coefficient (ICC) μ   Mean of outcome σb2   Between-cluster variance (σb between-cluster standard deviation) σw2   Within-cluster variance (σw within-cluster standard deviation) π   Probability of ‘success’ λ   Rate 

Notation in Chapter 4

p   Proportion of successes in sample s   Observed total standard deviation of outcome between participants 

Notation in Chapter 6

α   Constant in generalised estimating equations or mixed effects models β   Effect size eij   Residual for jth individual in the ith cluster xij   Dummy variable indicating intervention arm. When there are only two arms, xij takes the value 1 when the ith cluster is in the intervention arm, and 0 when the ith cluster is in the control arm Yij   Value of outcome for the jth individual in the ith cluster Yi   Mean value of outcome in the ith cluster μi   Mean effect of being in the ith cluster for cluster-specific models πij   Probability of ‘success’ for the ith individual in the jth cluster 

Notation in Chapter 7

ρm   Correlation between matched pairs cvc   Coefficient of variation of cluster sizes, cvcs   Coefficient of variation of cluster sizes within strata sc   Standard deviation of cluster sizes cv   Coefficient of variation for the outcome, or  cvm   Coefficient of variation for the outcome within a matched pair t   Number of person years per cluster th   Harmonic mean of the number of person years per cluster S   Number of strata Deff   Design effect ma    σb1   Between-cluster standard deviation in the intervention arm σb2   Between-cluster standard deviation in the control arm σbm   Between-cluster standard deviation for clusters within a matched pair σw   Within-cluster standard deviation (consistent with Donner) μ1   Mean in the intervention arm (note the subscripts for μ and π have a different meaning in Chapter 7 from their meaning in Chapters 6 and 8, although the usage in each chapter is consistent with the subscripts section at the top of this list ) μ2   Mean in the control arm (see note above) π1   Proportion in the intervention arm (see note above) π2   Proportion in the control arm (see note above) λ1   Rate in the intervention arm (see note above) λ2   Rate in the control arm (see note above) 

Notation in Chapter 8

ps   Probability that any two subjects from the same cluster have the same outcome po   Probability that any two subjects from different clusters have the same outcome MSB   Mean square between clusters MSW   Mean square within clusters  L   Total number of intervention arms Ns   Total number of individuals in a single population Nl   Total number of individuals in arm l of a trial Yli   Mean outcome in cluster i in intervention arm l Yl   Mean outcome in intervention arm l Yij   Value of the outcome for the jth individual in the ith cluster Yli   Mean outcome in cluster i in intervention arm l Yl   Mean outcome in intervention arm l L   Total number of intervention arms Ns   Total number of individuals in a single population Nl   Total number of individuals in arm l of a trial mli   Number of individuals in ith cluster in intervention arm l σ2   Total variance α   Constant in generalised estimating equations or mixed effects models μi   Mean effect of being in the ith cluster eij   Residual for jth individual in the ith cluster Π   Pi, mathematical quantity ∼3.14159 ρl   Proportion of total outcome variance that is due to between-cluster variation on a log scale σS   Variance of the ICC as estimated by Swiger’s formula σF   Variance of the ICC as estimated by Fisher’s formula mmax   Largest cluster size 

Table of cases: Trials used as examples in more than one chapter in the book

Kumasi trial: Health education to prevent stroke

Sections: 1.2, 1.3.3, 1.3.4, 2.2.3, 2.2.5, 2.2.7, 5.1.5, 5.1.8, 5.1.9, 7.5.1

Tables: 1.1, 2.9, 5.4

Guidelines to reduce inappropriate referral for x-ray

Sections: 1.3.1, 2.2.2.2, 7.4.1

Table: 1.2

OPERA: Physical activity in residential homes to prevent depression

Acronym: Older People’s Exercise intervention in Residential and nursing Accommodation

Sections: 1.3.1, 1.5.1, 2.2.2.1, 2.2.2.4, 2.2.2.5, 3.7, 5.1.8, 5.1.10, 5.2, 5.6, 6.5, 9.1.1, 9.1.2, 9.1.3, 9.3, 10.2.2, 10.4.3

Tables: 1.3, 2.7, 3.5, 5.9, 6.17, 9.1, 10.8

UK BEAM pilot trial: Active management of back pain

Acronym: United Kingdom Back pain Exercise And Manipulation

Sections: 1.3.1, 1.3.2, 2.3.4, 4.1.1, 4.1.2, 4.2.1, 10.2.1

Table: 1.4

ObaapaVitA: Vitamin A supplementation to reduce maternal and child mortality

(Obaapa means ‘good woman’)

Sections: 1.5.2, 1.5.6, 2.2.2.1

Tables: 1.5, 2.3

Promoting child safety by reducing baby walker use

Sections: 1.5.3, 1.5.4, 2.3.4, 7.1.2, 7.2.1, 10.4.13

Tables: 1.6, 7.2, 10.11

Trial of home blood pressure monitoring

Sections: 2.1.2, 10.4.10, 10.4.21

Table: 2.1

Diabetes care from diagnosis trial

Sections: 2.2.2.2, 2.2.6, 5.2, 7.1.3, 7.8.4

Tables: 2.4, 5.10, 7.10, 7.11

SHIP: Support following myocardial infarction

Acronym: Southampton Heart Integrated care Project

Sections: 1.5.5, 2.2.2.3, 2.2.6, 3.1, 8.3, 8.3.1

Tables: 2.5, 3.2

Educational intervention to increase depression awareness among students

Sections: 2.2.2.4, 10.4.13

Tables: 2.6, 10.14

Community-based interventions to promote blood pressure control

Sections: 2.2.2, 5.3.3

Table: 2.8

Ekjut project: participatory women’s groups to improve birth outcomes and maternal depression

Sections: 2.2.3, 7.3, 7.6

Tables: 2.10, 7.4

Trial of structured diabetes shared care

Sections: 2.3.1, 6.3.1, 10.4.18

Table: 2.11

IRIS: Training to increase identification and referral of victims of domestic violence

Acronym: Identification and Referral to Improve Safety

Sections: 2.3.2, 3.4, 3.7, 4.2.1, 4.2.4, 5.1.6, 6.1, 6.3.3.12, 6.5, 10.4.11

Tables: 2.12, 3.4, 4.1, 5.7, 6.13, 10.12

ELECTRA: Asthma liaison nurses to reduce unscheduled care

Acronym: East London randomised Controlled Trial for high Risk Asthma

Sections: 2.3.3, 5.1.6, 6.3.3.8, 6.3.3.12, 6.3.3.13, 6.6, 7.2.2, 7.4, 7.4.4, 7.6.2, 8.4.4, 10.4.2, 10.4.4, 10.4.8

Tables: 2.13, 5.6, 6.12, 7.3, 10.5

COMMIT: Community-based intervention to increase smoking quit rates

Acronym: COMMunity Intervention Trial

Sections: 3.1, 5.1.9, 6.3.1, 6.4.1, 7.4.2, 7.8.3, 9.3

Tables: 3.1, 5.8

Diabetes Manual trial: Manual and structured care to improve outcomes

Sections: 3.5, 4.1, 4.2.2, 4.3, 5.1.3, 6.3.3.3, 8.4.4, 10.4.2, 10.4.4

Tables: 3.9, 5.3, 6.7, 10.3

Multifaceted intervention to optimise antibiotic use in nursing homes

Sections: 3.4, 3.5, 3.5, 4.2.2

Tables: 3.7, 4.2

Pilot study for a falls prevention programme

Sections: 1.3.4, 1.5.7, 3.5, 4.1, 4.2.2, 7.1.4, 7.7.4

Tables: 4.3, 7.8

Educational intervention to improve intercultural communication

Sections: 5.1.2, 5.4, 6.3.3.4

Tables: 5.1, 6.8

Trial to improve screening for carriers of haemoglobin disorders

Sections: 5.2, 10.4.2, 10.4.13

Tables: 5.11, 10.7

ASSIST: Different interventions to promote secondary prevention of coronary heart disease

Acronym: Assessment of Implementation Strategies Trial

Sections: 5.3.2, 6.4.2, 7.4.3, 7.7.4, 7.9.5

Tables: 5.13, 6.15, 7.6

Two interventions to increase breast screening

Sections: 5.3.3, 6.4.2, 7.9.6

Tables: 5.14, 6.16, 7.12

Structured assessments of long term mentally ill

Sections: 5.1.6, 6.1, 6.3.2, 6.6, 7.6.2

Tables: 6.1, 6.2, 6.3

POST: Patient and practitioner postal prompts post-myocardial infarction

(POST comes from the word ‘postal’)

Sections: 6.3.1, 6.3.3.7, 8.2.1, 9.1.4, 10.4.15, 10.4.16, 10.4.17

Tables: 6.4, 6.10, 8.1, 9.2, 9.3, 10.15, 10.16

Clinical guidelines introduced with practice-based education

Sections: 6.3.2, 6.3.3.2, 6.3.3.8, 7.1.3

Table: 6.5

PRISM: Program of resources, information and support for mothers

Acronym: Program of Resources, Information and Support for Mothers

Sections: 7.8.3, 7.8.4, 10.2.2

Table: 7.9

Guidelines-based computerised decision support in cardiac rehabilitation

Sections: 6.6, 8.2.1

Table: 8.2

PALSA: Trial to improve detection of tuberculosis

Acronym: Practical Approach to Lung health in South Africa

Sections: 9.2.2, 10.4.13, 10.4.15

Table: 10.13

1

Introduction

Cluster randomised trials are trials in which groups (or clusters) of individuals are randomly allocated to different forms of treatment. In healthcare, the different forms of treatment are sometimes different drugs or, more commonly, different ways of managing a disease or promoting healthy living. These trials are in contrast to conventional randomised trials which randomise individuals to different treatments, classically comparing new drugs with a placebo. Cluster randomised trials are common in health services research. This is an area of research concerned with the way healthcare is delivered and with measures taken to prevent ill health and encourage healthy living. It covers a broad range of topics and is an important area in maintaining high standards in a modern health service. New initiatives or interventions in health care may be evaluated by comparing health outcomes in those that are exposed to the new initiative with outcomes in those receiving usual care or an alternative intervention. Since interventions often need to be introduced to a whole organisational unit such as a general practice or geographical area, cluster randomised trials are often the best method of evaluating such interventions.

There are many books written about trials in general, which explain in detail the key features of the design, conduct and analysis of randomised trials; but these are mainly concerned with trials which randomise individual patients to different interventions (Pocock, 1983; Matthews, 2000; Torgerson and Torgerson, 2008). There are now three books that describe the design, analysis and conduct of cluster randomised trials: Murray (1998), Donner and Klar (2000) and Hayes and Moulton (2009). These books have mainly concentrated on large community trials. Hayes and Moulton have a particular emphasis on trials in low-income countries where whole communities have been randomised. Since we have extensive experience in health services research, in this book we have focused on cluster randomised trials in this area, though we have used other examples where useful. This book is intended as a practical guide, written for researchers from the health professions, including doctors, psychologists, and allied health professionals, as well as statisticians, who are involved in the design, execution, analysis and reporting of cluster randomised trials. It is specifically written to address the issues arising from allocating groups of individuals, or clusters, to different interventions, and is primarily concerned with those aspects of cluster randomised trials which differ from randomised trials of individual subjects. Several trials are used as examples throughout the book. These are listed at the front of the book.