Methods for Meta-Analysis in Medical Research

, by Sutton, Alexander J.; Abrams, Keith R.; Jones, David R; Sheldon, Trevor A.; Song, Fujian

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ISBN: 9780471490661 | 0471490660
Cover: Hardcover
Copyright: 12/19/2000

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With meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field. Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments. The move towards evidence-based health care and practice is underpinned by the use of meta-analysis. This book: * Provides a thorough criticism and an up-to-date survey of meta-analysis methods * Emphasises the practical approach, and illustrates the methods by numerous examples * Describes the use of Bayesian methods in meta-analysis * Includes discussion of appropriate software for each analysis * Includes numerous references to more advanced treatment of specialist topics * Refers to software code used in the examples available on the authors' Web site Practising statisticians, statistically-minded clinicians and health research professionals will benefit greatly from the clear presentation and numerous examples. Medical researchers will grasp the basic principles of meta-analysis, and learn how to apply the various methods.

Preface

Acknowledgements

xvii

Part A: Meta-Analysis Methodology: The Basics

(160)

Introduction - Meta-analysis: Its Development and Uses

(14)

Evidence-based health care

(1)

Evidence-based everything!

(1)

Pulling together the evidence - systematic reviews

(3)

Why meta-analysis?

(4)

Aim of this book

(1)

Concluding remarks

(4)

References

(4)

Defining Outcome Measures used for Combining via Meta-analysis

(20)

Introduction

(1)

Non-comparative binary outcomes

(2)

Odds

(1)

Incidence rates

(1)

Comparative binary outcomes

(8)

The Odds ratio

(3)

Relative risk (or rate ratio/relative rate)

(2)

Risk differences between proportions (or the absolute risk reduction)

(2)

The number needed to treat

(1)

Comparisons of rates

(1)

Other scales of measurement used in summarizing binary data

(1)

Which scale to use?

(1)

Continuous data

(5)

Outcomes defined on their original metric (mean difference)

(2)

Outcomes defined using standardized mean differences

(2)

Ordinal outcomes

(1)

Summary/Discussion

(4)

References

(3)

Assessing Between Study Heterogeneity

(20)

Introduction

(1)

Hypothesis tests for presence of heterogeneity

(3)

Standard X2 test

(1)

Extensions/alternative tests

(1)

Example: Testing for heterogeneity in the cholesterol lowering trial dataset

(1)

Graphical informal tests/explorations of heterogeneity

(7)

Plot of normalized (z) scores

(1)

Forest plot

(4)

Radial plot (Galbraith diagram)

(1)

L'Abbe plot

(1)

Possible causes of heterogeneity

(2)

Specific factors that may cause heterogeneity in RCTs

(1)

Methods for investigating and dealing with sources of heterogeneity

(3)

Change scale of outcome variable

(1)

Include covariates in a regression model (meta-regression)

(1)

Exclude studies

(1)

Analyse groups of studies separately

(1)

Use of random effects models

(1)

Use of mixed-effect models

(1)

The validity of pooling studies with heterogeneous outcomes

(1)

Summary/Discussion

(4)

References

(3)

Fixed Effects Methods for Combining Study Estimates

(16)

Introduction

(1)

General fixed effect model - the inverse variance-weighted method

(5)

Example: Combining odds ratios using the inverse variance-weighted method

(3)

Example: Combining standardized mean differences using a continuous outcome scale

(1)

Specific methods for combining odds ratios

(7)

Mantel-Haenszel method for combining odds ratios

(2)

Peto's method for combining odds ratios

(2)

Combining odds ratios via maximum-likelihood techniques

(1)

Exact methods of interval estimation

(1)

Discussion of the relative merits of each method

(1)

Summary/Discussion

(3)

References

(2)

Random Effects Models for Combining Study Estimates

(14)

Introduction

(1)

Algebraic derivation for random effects models by the weighted method

(1)

Maximum likelihood and restricted maximum likelihood estimate solutions

(1)

Comparison of estimation methods

(1)

Example: Combining the cholesterol lowering trials using a random effects model

(4)

Extensions to the random effects model

(3)

Including uncertainty induced by estimating the between study variance

(1)

Exact approach to random effects meta-analysis of binary data

(1)

Miscellaneous extensions to the random effects model

(1)

Comparison of random with fixed effect models

(1)

Summary/Discussion

(3)

References

(3)

Exploring Between Study Heterogeneity

(22)

Introduction

(1)

Subgroup analyses

(5)

Example: Stratification by study characteristics

(1)

Example: Stratification by patient characteristics

(4)

Regression models for meta-analysis

(11)

Meta-regression models (fixed-effects regression)

(2)

Meta-regression example: a meta-analysis of Bacillus Calmette-Guerin (BCG) vaccine for the prevention of tuberculosis (TB)

(2)

Mixed effect models (random-effects regression)

(2)

Mixed model example: A re-analysis of Bacillus Calmette-Guerin (BCG) vaccine for the prevention of tuberculosis (TB) trials

(1)

Mixed modelling extensions

(5)

Summary/Discussion

104

(5)

References

105

(4)

Publication Bias

109

(24)

Introduction

109

(1)

Evidence of publication and related bias

110

(2)

Survey of authors

110

(1)

Published versus registered trials in a meta-analysis

110

(1)

Follow-up of cohorts of registered studies

111

(1)

Non-empirical evidence

111

(1)

Evidence of language bias

111

(1)

The seriousness and consequences of publication bias for meta-analysis

112

(1)

Predictors of publication bias (factors effecting the probability a study will get published)

112

(1)

Identifying publication bias in a meta-analysis

112

(7)

The funnel plot

113

(3)

Rank correlation test

116

(1)

Linear regression test

117

(2)

Other methods to detect publication bias

119

(1)

Practical advice on methods for detecting publication bias

119

(1)

Taking into account publication bias or adjusting the results of a meta-analysis in the presence of publication bias

119

(7)

Analysing only the largest studies

120

(1)

Rosenthal's `file drawer' method

120

(2)

Models which estimate the number of unpublished studies, but do not adjust

122

(1)

Selection models using weighted distribution theory

123

(1)

The `Trim and Fill' method

123

(2)

The sensitivity approach of Copas

125

(1)

Broader perspective solutions to publication bias

126

(1)

Prospective registration of trials

126

(1)

Changes in publication process and journals

126

(1)

Including unpublished information

127

(1)

Summary/Discussion

127

(6)

References

128

(5)

Study Quality

133

(14)

Introduction

133

(1)

Methodological factors that may affect the quality of studies

134

(3)

Experimental studies

135

(1)

Observational Studies

136

(1)

Incorporating study quality into a meta-analysis

137

(6)

Graphical plot

137

(1)

Cumulative methods

138

(1)

Regression model

138

(2)

Weighting

140

(2)

Excluding studies

142

(1)

Sensitivity analysis

143

(1)

Practical implementation

143

(1)

Summary/Discussion

144

(3)

References

144

(3)

Sensitivity Analysis

147

(6)

Introduction

147

(1)

Sensitivity of results to inclusion criteria

147

(3)

Sensitivity of results to meta-analytic methods

150

(1)

Assessing the impact of choice of study weighting

150

(1)

Summary/Discussion

151

(2)

References

151

(2)

Reporting the Results of a Meta-analysis

153

(8)

Introduction

153

(1)

Overview and structure of a report

154

(1)

Graphical displays used for reporting the findings of a meta-analysis

155

(3)

Forest plots

155

(2)

Radial plots

157

(1)

Funnel plots

157

(1)

Displaying the distribution of effect size estimates

158

(1)

Graphs investigating length of follow-up

158

(1)

Summary/Discussion

158

(3)

References

158

(3)

Part B: Advanced and Specialized Meta-analysis Topics

161

(140)

Bayesian Methods in Meta-analysis

163

(28)

Introduction

163

(1)

Bayesian methods in health research

163

(6)

General introduction

163

(3)

General advantages/disadvantages of Bayesian methods

166

(1)

Example: Bayesian analysis of a single trial using a normal conjugate model

167

(2)

Bayesian meta-analysis of normally distributed data

169

(2)

Example: Combining trials with continuous outcome measures using Bayesian methods

171

(1)

Bayesian meta-analysis of binary data

171

(4)

Example: Combining binary outcome measures using Bayesian methods

173

(2)

Empirical Bayes methods in meta-analysis

175

(1)

Advantages/disadvantages of Bayesian methods in meta-analysis

176

(3)

Advantages

176

(2)

Disadvantages

178

(1)

Extensions and specific areas of application

179

(4)

Incorporating study quality

179

(1)

Inclusion of covariates

180

(1)

Model selection

180

(1)

Hierarchical models

181

(1)

Sensitivity analysis

181

(1)

Comprehensive modelling

182

(1)

Other developments

183

(1)

Summary/Discussion

183

(8)

References

183

(8)

Meta-analysis of Individual Patient Data

191

(8)

Introduction

191

(2)

Procedural methodology

193

(1)

Data collection

193

(1)

Checking data

193

(1)

Issues involved in carrying out IPD meta-analysis

193

(1)

Comparing meta-analysis using IPD or summary data?

194

(1)

Combining individual patient and summary data

195

(1)

Summary/Discussion

196

(3)

References

196

(3)

Missing Data

199

(6)

Introduction

199

(1)

Reasons for missing data

200

(1)

Categories of missing data at the study level

200

(1)

Analytic methods for dealing with missing data

201

(2)

General missing data methods which can be applied in the meta-analysis context

201

(1)

Missing data methods specific to meta-analysis

202

(1)

Example: Dealing with missing standard deviations of estimates in a meta-analysis

202

(1)

Bayesian methods for missing data

203

(1)

Summary/Discussion

203

(2)

References

204

(1)

Meta-analysis of Different Types of Data

205

(24)

Introduction

205

(1)

Combining ordinal data

205

(1)

Issues concerning scales of measurement when combining data

206

(3)

Transforming scales, maintaining same data type

207

(1)

Binary outcome data reported on different scales

207

(1)

Combining studies whose outcomes are reported using different data types

208

(1)

Combining summaries of binary outcomes with those of continuous outcomes

208

(1)

Non-parametric method of combining different data type effect measures

208

(1)

Meta-analysis of diagnostic test accuracy

209

(6)

Combining binary test results

209

(6)

Combining ordered categorical test results

215

(1)

Combining continuous test results

215

(1)

Meta-analysis using surrogate markers

215

(1)

Combining a number of cross-over trials using the patient preference outcome

216

(1)

Vote-counting methods

217

(1)

Combining p-values/significance levels

218

(5)

Minimum p method

219

(1)

Sum of z's method

220

(1)

Sum of logs method

220

(1)

Logit method

220

(1)

Other methods of combining significance levels

220

(1)

Appraisal of the methods

221

(1)

Example of combining p-values

221

(2)

Novel applications of meta-analysis using non-standard methods or data

223

(1)

Summary/Discussion

223

(6)

References

223

(6)

Meta-analysis of Multiple and Correlated Outcome Measures

229

(10)

Introduction

229

(1)

Combining multiple p-values

230

(1)

Method for reducing multiple outcomes to a single measure for each study

231

(1)

Development of a multivariate model

231

(5)

Model of Raudenbush et al.

231

(1)

Model of Gleser and Olkin

232

(1)

Multiple outcome model for clinical trials

232

(1)

Random effect multiple outcome regression model

232

(1)

DuMouchel's extended model for multiple outcomes

233

(1)

Illustration of the use of multiple outcome models

233

(3)

Summary/Discussion

236

(3)

References

236

(3)

Meta-analysis of Epidemiological and Other Observational Studies

239

(20)

Introduction

239

(1)

Extraction and derivation of study estimates

240

(6)

Scales of measurement used to report and combine observational studies

243

(1)

Data manipulation for data extraction

243

(1)

Methods for transforming and adjusting reported results

244

(2)

Analysis of summary data

246

(2)

Heterogeneity of observational studies

246

(1)

Fixed or random effects?

247

(1)

Weighting of observational studies

247

(1)

Methods for combining estimates of observational studies

247

(1)

Dealing with heterogeneity and combining the OC and breast cancer studies

248

(1)

Reporting the results of meta-analysis of observational studies

248

(1)

Use of sensitivity and influence analysis

248

(1)

Study quality considerations for observational studies

249

(1)

Other issues concerning meta-analysis of observational studies

250

(4)

Analysing individual patient data from observational studies

250

(1)

Combining dose-reponse data

251

(2)

Meta-analysis of single case research

253

(1)

Unresolved issues concerning the meta-analysis of observational studies

254

(1)

Summary/Discussion

255

(4)

References

255

(4)

Generalized Synthesis of Evidence-Combining Different Sources of Evidence

259

(18)

Introduction

259

(1)

Incorporating Single-arm studies: models for incorporating historical controls

259

(3)

Example

260

(2)

Combining matched and unmatched data

262

(1)

Approaches for combining studies containing multiple and/or different treatment arms

263

(2)

Approach of Gleser and Olkin

264

(1)

Models of Berkey et al.

264

(1)

Method of Higgins

264

(1)

Mixed model of DuMouchel

264

(1)

The confidence profile method

265

(1)

Cross-design synthesis

266

(7)

Beginnings

267

(1)

Bayesian hierarchical models

267

(4)

Grouped random effects models of Larose and Dey

271

(1)

Synthesizing studies with disparate designs to assess the exposure effects on the incidence of a rare adverse event

271

(1)

Combining the results of cancer studies in humans and other species

272

(1)

Combining biochemical and epidemiological evidence

272

(1)

Combining information from disparate toxicological studies using stratified ordinal regression

272

(1)

Summary/Discussion

273

(4)

References

273

(4)

Meta-analysis of Survival Data

277

(10)

Introduction

277

(1)

Inferring/estimating and combining (log) hazard ratios

278

(1)

Calculation of the `log-rank' odds ratio

278

(1)

Calculation of pooled survival rates

279

(1)

Method of Hunink and Wong

279

(1)

Iterative generalized least squares for meta-analysis of survival data at multiple times

280

(2)

Application of the model

281

(1)

Identifying prognostic factors using a log (relative risk) measure

282

(1)

Combining quality of life adjusted survival data

282

(1)

Meta-analysis of survival data using individual patient data

283

(1)

Pooling independent samples of survival data to form an estimator of the common survival function

283

(1)

Is obtaining and using survival data necessary?

283

(1)

Summary/Discussion

284

(3)

References

284

(3)

Cumulative Meta-analysis

287

(8)

Introduction

287

(1)

Example: Ordering by date of publication

288

(2)

Using study characteristics other than date of publication

290

(1)

Example: Ordering the cholesterol trials by baseline risk in the control group

290

(1)

Bayesian approaches

291

(1)

Issues regarding uses of cumulative meta-analysis

291

(1)

Summary/Discussion

292

(3)

References

292

(3)

Miscellaneous and Developing Areas of Application in Meta-analysis

295

(6)

Introduction

295

(1)

Alternatives to conventional meta-analysis

295

(2)

Estimating and extrapolating a response surface

295

(1)

Odd man out method

296

(1)

Best evidence synthesis

296

(1)

Developing areas

297

(4)

Prospective meta-analysis

297

(1)

Economic evaluation through meta-analysis

298

(1)

Combining meta-analysis and decision analysis

299

(1)

Net benefit model synthesizing disparate sources of information

299

(1)

References

299

(2)

Appendix I: Software Used for the Examples in this Book

301

(8)

Subject index

309

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Methods for Meta-Analysis in Medical Research

Summary

Author Biography

Table of Contents

Supplemental Materials