Meta-analysis of controlled clinical trials:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Chichester
John Wiley & Sons
2002
|
| Series: | Statistics in practice
|
| Subjects: | |
| Online Access: | Table of contents Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XIV, 336 S. graph. Darst. |
| ISBN: | 0471983705 |
Staff View
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Record in the Search Index
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| adam_text | Contents
Preface xiii
1 Introduction 1
1.1 Theroleofmeta analysis 1
1.2 Retrospective and prospective meta analyses 3
]. 3 Fixed effects versus random effects 5
1.4 Individual patient data versus summary statistics 6
1.5 Multicentre trials and meta analysis 7
1.6 The structure of this book 8
2 Protocol development 11
2.1 Introduction 11
2.2 Background 12
2.3 Objectives 12
2.4 Outcome measures and baseline information 13
2.5 Sourcesofdata 13
2.6 Study selection 14
2.7 Data extraction 15
2.8 Statistical analysis If)
2.8.1 Analysis population 16
2.8.2 Missing data at the subject level 17
2.8.3 Analysis of individual trials 18
2.8.4 Meta analysis model 19
2.8.5 Estimation and hypothesis testing 19
2.8.6 Testing for heterogeneity 19
2.8.7 Exploration of heterogeneity 20
2.9 Sensitivity analyses 20
2.10 Presentation of results 21
3 Estimating the treatment difference in an individual
trial 23
3.1 Introduction 2 3
3.2 Binary data 25
3.2.1 Example: Stroke in hypertensive patients 25
3.2.2 Measurement of treatment difference 25
vii
viii Contents
3.3 Survival data 32
3.3.1 Example: Mortality following myocardial infarction 3 2
3.3.2 Measurement of treatment difference 33
3.4 Interval censored survival data 38
3.4.1 Example: Ulcer recurrence 38
3.4.2 Measurement of treatment difference 39
3.5 Ordinal data 42
3.5.1 Example: Global impression of change in Alzheimer s disease 42
3.5.2 Measurement of treatment difference 42
3.6 Normally distributed data 49
3.6.1 Example: Recovery time after anaesthesia 49
3.6.2 Measurement of treatment difference 50
4 Combining estimates of a treatment difference
across trials 57
4.1 Introduction 57
4.2 A general fixed effects parametric approach 58
4.2.1 A fixed effects meta analysis model 58
4.2.2 Estimation and hypothesis testing of the treatment difference 58
4.2.3 Testing for heterogeneity across studies 60
4.2.4 Obtaining the statistics via weighted least squares regression 61
4.2.5 Example: Stroke in hypertensive patients 61
4.2.6 Example: Mortality following myocardial infarction 69
4.2.7 Example: Ulcer recurrence 73
4.2.8 Example: Global impression of change in Alzheimer s disease 78
4.2.9 Example: Recovery time after anaesthesia 82
4.3 A general random effects parametric approach 88
4.3.1 A random effects meta analysis model 88
4.3.2 Estimation and hypothesis testing of the treatment difference 88
4.3.3 Estimation of x2 using the method of moments 90
4.3.4 Obtaining the statistics via weighted least squares regression 91
4.3.5 Example: Mortality following myocardial infarction 91
4.3.6 Example: Global impression of change in Alzheimer s disease 93
4.3.7 Example: Recovery time after anaesthesia 93
4.3.8 A likelihood approach to the estimation oft2 94
4.3.9 Allowing for the estimation oft2 97
5 Meta analysis using individual patient data 99
5.1 Introduction 99
5.2 Fixed effects models for normally distributed data 100
5.2.1 A lixed effects meta analysis model 100
5.2.2 Estimation and hypothesis testing 101
5.2.3 Testing for heterogeneity in the absolute mean difference across studies 103
5.2.4 Example: Recovery time after anaesthesia 103
5.2.5 Modelling of individual patient data versus combining study estimates 105
5.2.6 Heterogeneity in the variance parameter across studies 105
5.3 Fixed effects models for binary data ] 0 7
5.3.1 A iixed effects meta analysis model 107
5.3.2 Estimation and hypothesis testing 108
5.3.3 Testing for heterogeneity in the log odds ratio across studies 109
Contents ix
5.3.4 Example: Stroke in hypertensive patients 110
5.3.5 Modelling of individual patient data versus combining study estimates 110
5.4 Fixed effects models for ordinal data 111
5.4.1 A fixed effects meta analysis model 111
5.4.2 Estimation and hypothesis testing 113
5.4.3 Testing for heterogeneity in the log odds ratio across studies 115
5.4.4 Example: Global impression of change in Alzheimer s disease 116
5.4.5 Modelling of individual patient data versus combining study estimates 117
5.4.6 Testing the assumption of proportional odds between treatments 117
5.4.7 A proportional odds model for studies and treatments 119
5.5 Fixed effects models for survival data 120
5.5.1 A fixed effects meta analysis model 120
5.5.2 Estimation and hypothesis testing 121
5.5.3 Testing for heterogeneity in the log hazard ratio across studies 122
5.5.4 Example: Mortality following myocardial infarction 122
5.5.5 Modelling of individual patient data versus combining study estimates 12 3
5.5.6 Testing the assumption of proportional hazards between treatments 124
5.5.7 A proportional hazards model for studies and treatments 124
5.6 Fixed effects models for interval censored survival data 126
5.6.1 A fixed effects meta analysis model 126
5.6.2 Estimation and hypothesis testing 127
5.6.3 Testing for heterogeneity in the log hazard ratio across studies 127
5.6.4 Example: Ulcer recurrence 128
5.6.5 Modelling of individual patient data versus combining study estimates 128
5.6.6 Testing the assumption of proportional hazards between treatments
across timepoints 129
5.6.7 A proportional hazards model for studies and treatments 130
5.7 The treatment difference as a random effect 1 31
5.8 Random effects models for normally distributed data 131
5.8.1 A random effects meta analysis model 1 31
5.8.2 Estimation and hypothesis testing 132
5.8.3 Example: Recovery time after anaesthesia 133
5.8.4 The connection between the multilevel model and the traditional mixed
effects linear model 1 34
5.9 Random effects models for binary data 136
5.9.1 A random effects meta analysis model 136
5.9.2 Estimation and hypothesis testing 136
5.9.3 Example: Pre edampsia 1 iV
5.10 Random effects models for other data types 142
5.10.1 A random effects meta analysis model for ordinal data 142
5.10.2 Example: Global impression of change in Alzheimer s disease 143
5.11 Random study effects 144
5.11.1 Random study and study by treatment effects: normally distributed data 145
5.11.2 Example: Recovery time after anaesthesia 146
5.11.3 Random study and study by treatment effects: other delta types 147
5.12 Comparisons between the various models 147
6 Dealing with heterogeneity 151
6.1 Introduction 1 51
6.2 The use of a formal test for heterogeneity 1 52
6.3 The choice between a fixed effects and a random effects model 1 5 3
x Contents
6.4 When not to present an overall estimate of treatment difference 154
6.5 The choice of an appropriate measure of treatment difference 156
6.6 Meta regression using study estimates of treatment difference 157
6.6.1 Example: Global impression of change in Alzheimer s disease 160
6.6.2 Example: Recovery time after anaesthesia 161
6.6.3 Extension to study estimates of treatment difference from subgroups 163
6.7 Patient level covariates 165
6.7.1 Adjustment for imbalance in prognostic factors 165
6.7.2 Investigation of potential sources of heterogeneity 166
6.7.3 Example: Global impression of change in Alzheimer s disease 167
6.7.4 Meta regression using individual patient data 168
6.7.5 Example: Recovery time after anaesthesia 168
6.8 An investigation of heterogeneity: Aspirin in coronary heart disease 170
6.9 A strategy for dealing with heterogeneity 174
7 Presentation and interpretation of results 175
7.1 Introduction 175
7.2 Structure of a report 176
7.2.1 Introduction 176
7.2.2 Methods 176
7.2.3 Results 178
7.2.4 Discussion 181
7.3 Graphical presentation 182
7.3.1 A confidence interval plot 183
7.3.2 A radial plot 186
7.4 Clinically useful measures of treatment difference 189
7.4.1 Simple transformations ofthe treatment difference parameter 190
7.4.2 Probability of doing better on treatment than on control 192
7.4.3 The number needed to treat 194
8 Selection bias 197
8.1 Introduction 197
8.2 An investigation of publication bias: Intravenous magnesium
following acute myocardial infarction 199
8.3 A funnel plot 199
8.4 Statistical methods for the detection and correction of publication bias 205
8.4.1 A test of funnel plot asymmetry 205
8.4.2 Rosenthal s file drawer method 208
8.4.3 Models for the probability of selection 210
8.5 Bias due to selective reporting within studies 213
9 Dealing with non standard data sets 215
9.1 Introduction 215
9.2 No events in treatment arms of individual trials 216
9.3 Different rating scales or methods of assessment across trials 220
9.4 Different times of assessment across trials 225
9.5 Combining trials which report different summary statistics 228
9.5.1 Continuous outcomes 228
9.5.2 Ordinal data 231
9.5.3 Survival data 233
Contents xi
9.6 Imputation of the treatment difference and its variance 2 3 3
9.6.1 Absolute mean difference for continuous outcomes 233
9.6.2 The log hazard ratio for survival data 23 5
9.7 Combining summary statistics and individual patient data 2 36
9.8 Combining p values 237
10 Inclusion of trials with different study designs 241
10.1 Introduction 241
10.2 More than two treatment groups 242
10.2.1 A fixed effects meta analysis model 242
10.2.2 A random effects meta analysis model 24 3
10.2.3 Random study effects 244
10.2.4 Example: First bleeding in cirrhosis 245
10.3 Dose response relationships 249
10.4 Multicentre trials 25 3
10.5 Cross over trials 254
10.6 Sequential trials 25 5
11 A Bayesian approach to meta analysis 259
11.1 Introduction 25*4
11.2 A Bayesian approach to the random effects model for study estimates 261
11.3 Choice of the prior distribution 26 3
11.4 Implementation using the BUGS software 265
11.4.1 Example: Recovery time after anaesthesia 267
11.5 Bayesian meta regression 26X
11.6 A Bayesian random effects model based on individual patient data 2 7(1
11.6.1 Normally distributed data 271
11.6.2 Binary data 27 3
11.6.3 Ordinal data 274
11.6.4 Study level and patient level covariates 276
11.6.5 Random study effects 276
11.7 Incorporating data from other treatment comparisons 279
11.8 An empirical prior distribution for the heterogeneity parameter 2X2
12 Sequential methods for meta analysis 285
12.1 Introduction 2X5
12.2 A proactive cumulative meta analysis 2X6
12.2.1 Choice of a sequential design 2X7
12.2.2 A tixed effects model 29 1
12.2.3 A random effects model 292
12.2.4 Example: The triangular test for a primary efficacy outcome 29 3
12.2.5 Estimation of the heterogeneity parameter 296
12.3 A reactive cumulative meta analysis 296
12.3.1 Example: Endoscopic haemostasis for bleeding peptic ulcers 29 7
12.3.2 Alternative approaches to a formal stopping rule 30 i
Appendix: Methods of estimation and hypothesis testing 305
A.I Introduction 5 5
A.2 The method of least squares 306
xii Contents
A.3 The method of weighted least squares 308
A.4 Iterative maximum likelihood estimation 308
A. 5 Likelihood, efficient score and Fisher s information 311
A. 6 Iteratively weighted least squares 312
A.7 Maximum likelihood methods for generallinear mixed models 314
A. 8 Iterative generalized least squares for normally distributed data 316
A.9 Marginal quasi likelihood and penalized quasi likelihood methods
for discrete data 317
References 321
Index 329
|
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| spelling | Whitehead, Anne Verfasser aut Meta-analysis of controlled clinical trials Anne Whitehead Meta analysis of controlled clinical trials Chichester John Wiley & Sons 2002 XIV, 336 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Statistics in practice Includes bibliographical references and index Clinical trials gtt Meta-analyse gtt Clinical trials Statistical methods Controlled Clinical Trials as Topic statistics & numerical data Meta-Analysis as Topic Meta-analysis Kontrollierte klinische Studie (DE-588)4202997-1 gnd rswk-swf Metaanalyse (DE-588)4169552-5 gnd rswk-swf Kontrollierte klinische Studie (DE-588)4202997-1 s Metaanalyse (DE-588)4169552-5 s DE-604 http://www.loc.gov/catdir/toc/wiley023/2002072173.html Table of contents HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009887041&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Whitehead, Anne Meta-analysis of controlled clinical trials Clinical trials gtt Meta-analyse gtt Clinical trials Statistical methods Controlled Clinical Trials as Topic statistics & numerical data Meta-Analysis as Topic Meta-analysis Kontrollierte klinische Studie (DE-588)4202997-1 gnd Metaanalyse (DE-588)4169552-5 gnd |
| subject_GND | (DE-588)4202997-1 (DE-588)4169552-5 |
| title | Meta-analysis of controlled clinical trials |
| title_alt | Meta analysis of controlled clinical trials |
| title_auth | Meta-analysis of controlled clinical trials |
| title_exact_search | Meta-analysis of controlled clinical trials |
| title_full | Meta-analysis of controlled clinical trials Anne Whitehead |
| title_fullStr | Meta-analysis of controlled clinical trials Anne Whitehead |
| title_full_unstemmed | Meta-analysis of controlled clinical trials Anne Whitehead |
| title_short | Meta-analysis of controlled clinical trials |
| title_sort | meta analysis of controlled clinical trials |
| topic | Clinical trials gtt Meta-analyse gtt Clinical trials Statistical methods Controlled Clinical Trials as Topic statistics & numerical data Meta-Analysis as Topic Meta-analysis Kontrollierte klinische Studie (DE-588)4202997-1 gnd Metaanalyse (DE-588)4169552-5 gnd |
| topic_facet | Clinical trials Meta-analyse Clinical trials Statistical methods Controlled Clinical Trials as Topic statistics & numerical data Meta-Analysis as Topic Meta-analysis Kontrollierte klinische Studie Metaanalyse |
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