JOACHIM HARTUNG, PhD, is Professor in the Department of Statistics at the Dortmund University of Technology, Germany. He has published several books and two dozen journal articles in the field of statistics. GUIDO KNAPP, PhD, is Assistant Professor in the Department of Statistics at the Dortmund University of Technology, Germany. Dr. Knapp's areas of research interest include variance component models, error components regression models, meta-analysis, and flexible design in clinical trials. BIMAL K. SINHA, PhD, is Presidential Research Professor of Statistics in the Department of Mathematics and Statistics at the University of Maryland at Baltimore County (UMBC). A Fellow of both the Institute of Mathematical Statistics and the American Statistical Association, Dr. Sinha's research specializes in the areas of multivariate analysis, mixed linear models, decision theory, robustness, and environmental statistics.
Preface | p. xiii |
Introduction | p. 1 |
Various Measures of Effect Size | p. 13 |
Effect Size Based on Means | p. 13 |
Effect Size Based on Proportions | p. 17 |
Effect Size Based on [open phi] Coefficient and Odds Ratio | p. 19 |
Effect Size Based on Correlation | p. 22 |
Combining Independent Tests | p. 25 |
Introduction | p. 25 |
Description of Combined Tests | p. 27 |
Methods of Combining Effect Sizes | p. 35 |
Inference about a Common Mean of Several Univariate Normal Populations | p. 43 |
Results on Common Mean Estimation | p. 45 |
Small-Sample Comparison of [mu subscript GD] with Other Estimators | p. 45 |
Properties of [mu subscript GD] | p. 48 |
Asymptotic Comparison of Some Estimates of Common Mean for [kappa] = 2 Populations | p. 52 |
Confidence Intervals for the Common Mean | p. 54 |
Approximate Confidence Intervals | p. 55 |
Exact Confidence Intervals | p. 56 |
Applications | p. 59 |
Theory of Fisher's Method | p. 60 |
Tests of Homogeneity in Meta-Analysis | p. 63 |
Model and Test Statistics | p. 64 |
An Exact Test of Homogeneity | p. 67 |
Applications | p. 68 |
One-Way Random Effects Model | p. 73 |
Introduction | p. 73 |
Homogeneous Error Variances | p. 76 |
Test for [sigma subscript a superscript 2] = 0 | p. 76 |
Approximate Tests for H[subscript 0]: [sigma subscript a superscript 2] = [delta] > 0 and Confidence Intervals for [sigma subscript a superscript 2] | p. 77 |
Exact Test and Confidence Interval for [sigma subscript a superscript 2] Based on a Generalized P-value Approach | p. 81 |
Tests and Confidence Intervals for [mu] | p. 85 |
Heterogeneous Error Variances | p. 85 |
Tests for H[subscript 0]: [sigma subscript a superscript 2] = 0 | p. 85 |
Tests for H[subscript 0]: [sigma subscript a superscript 2] = [delta] > 0 | p. 85 |
Nonnegative Estimation of [sigma subscript a superscript 2] and Confidence Intervals | p. 87 |
Inference about [mu] | p. 93 |
Combining Controlled Trials with Normal Outcomes | p. 97 |
Difference of Means | p. 98 |
Approximate Confidence Intervals for the Common Mean Difference | p. 100 |
Exact Confidence Intervals for the Common Mean Difference | p. 100 |
Testing Homogeneity | p. 102 |
Analysis in the Random Effects Model | p. 103 |
Standardized Difference of Means | p. 107 |
Ratio of Means | p. 110 |
Combining Controlled Trials with Discrete Outcomes | p. 113 |
Binary Data | p. 116 |
Effect Size Estimates | p. 116 |
Homogeneity Tests | p. 118 |
Binomial-Normal Hierarchical Models in Meta-Analysis | p. 118 |
An Example for Combining Results from Controlled Clinical Trials | p. 120 |
An Example for Combining Results from Observational Studies | p. 121 |
Ordinal Data | p. 122 |
Proportional Odds Model | p. 123 |
Agresti's [alpha] | p. 124 |
An Example of Combining Results from Controlled Clinical Trials | p. 124 |
Meta-Regression | p. 127 |
Model with One Covariate | p. 128 |
Model with More Than One Covariate | p. 132 |
Further Extensions and Applications | p. 136 |
Multivariate Meta-Analysis | p. 139 |
Combining Multiple Dependent Variables from a Single Study | p. 141 |
Modeling Multivariate Effect Sizes | p. 143 |
Multiple-Endpoint Studies | p. 144 |
Multiple-Treatment studies | p. 149 |
Bayesian Meta-Analysis | p. 155 |
A General Bayesian Model for Meta-Analysis under Normality | p. 156 |
Further Examples of Bayesian Analyses | p. 159 |
A Unified Bayesian Approach to Meta-Analysis | p. 164 |
Further Results on Bayesian Meta-Analysis | p. 167 |
Publication Bias | p. 171 |
Recovery of Interblock Information | p. 179 |
Notation and Test Statistics | p. 180 |
BIBD with Fixed Treatment Effects | p. 183 |
Combined Tests When b > v | p. 184 |
Combined Tests When b = v | p. 187 |
A Numerical Example | p. 188 |
Combination of Polls | p. 191 |
Formulation of the Problem | p. 192 |
Meta-Analysis of Polls | p. 196 |
Estimation of [theta] | p. 196 |
Confidence Interval for [theta] | p. 198 |
Hypothesis Testing for [theta] | p. 200 |
Vote Counting Procedures | p. 203 |
Computational Aspects | p. 213 |
Extracting Summary Statistics | p. 213 |
Combining Tests | p. 214 |
Generalized P-values | p. 215 |
Combining Effect Sizes | p. 217 |
Graphics | p. 218 |
Sample Program in R | p. 218 |
Sample Program in SAS | p. 220 |
Data Sets | p. 225 |
Validity Studies | p. 225 |
Effects of Teacher Expectance on Pupil IQ | p. 226 |
Dentifrice Data | p. 227 |
Effectiveness of Amlodipine on Work Capacity | p. 228 |
Effectiveness of Cisapride on the Treatment of Nonulcer Dyspepsia | p. 229 |
Second-hand Smoking | p. 230 |
Effectiveness of Misoprostol in Preventing Gastrointestinal Damage | p. 230 |
Prevention of Tuberculosis | p. 230 |
References | p. 233 |
Index | p. 245 |
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