Meta analysis : a guide to calibrating and combining statistical evidence
 Author/Creator
 Kulinskaya, Elena.
 Language
 English.
 Imprint
 Chichester, West Sussex ; Hoboken, NJ : John Wiley & Sons, c2008.
 Physical description
 xiv, 260 p. : ill. ; 23 cm.
 Series
 Wiley series in probability and statistics.
Access
Contributors
 Contributor
 Morgenthaler, Stephan.
 Staudte, Robert G.
Contents/Summary
 Bibliography
 Includes bibliographical references (p. [253]256) and index.
 Contents

 Preface. Part I The Methods. 1 What can the reader expect from this book? 1.1 A calibration scale for evidence. 1.2 The efficacy of glass ionomer versus resin sealants for prevention of caries. 1.3 Measures of effect size for two populations. 1.4 Summary. 2 Independent measurements with known precision. 2.1 Evidence for onesided alternatives. 2.2 Evidence for twosided alternatives. 2.3 Examples. 3 Independent measurements with unknown precision. 3.1 Effects and standardized effects. 3.2 Paired comparisons. 3.3 Examples. 4 Comparing treatment to control. 4.1 Equal unknown precision. 4.2 Differing unknown precision. 4.3 Examples. 5 Comparing K treatments. 5.1 Methodology. 5.2 Examples. 6 Evaluating risks. 6.1 Methodology. 6.2 Examples. 7 Comparing risks. 7.1 Methodology. 7.2 Examples. 8 Evaluating Poisson rates. 8.1 Methodology. 8.2 Example. 9 Comparing Poisson rates. 9.1 Methodology. 9.2 Example. 10 Goodnessoffit testing. 10.1 Methodology. 10.2 Example. 11 Evidence for heterogeneity of effects and transformed effects. 11.1 Methodology. 11.2 Examples. 12 Combining evidence: fixed standardized effects model. 12.1 Methodology. 12.2 Examples. 13 Combining evidence: random standardized effects mode. 13.1 Methodology. 13.2 Example. 14 Metaregression. 14.1 Methodology. 14.2 Commonly encountered situations. 14.3 Examples. 15 Accounting for publication bias. 15.1 The downside of publishing. 15.2 Examples. Part II The Theory. 16 Calibrating evidence in a test. 16.1 Evidence for onesided alternatives. 16.2 Random pvalue behavior. 16.3 Publication bias. 16.4 Comparison with a Bayesian calibration. 16.5 Summary. 17 The basics of variance stabilizing transformations. 17.1 Standardizing the sample mean. 17.2 Variance stabilizing transformations. 17.3 Poisson model example. 17.4 Twosided evidence from onesided evidence. 17.5 Summary. 18 Onesample binomial tests. 18.1 Variance stabilizing the risk estimator. 18.2 Confidence intervals for p. 18.3 Relative risk and odds ratio. 18.4 Confidence intervals for small risks p. 18.5 Summary. 19 Twosample binomial tests. 19.1 Evidence for a positive effect. 19.2 Confidence intervals for effect sizes. 19.3 Estimating the risk difference. 19.4 Relative risk and odds ratio. 19.5 Recurrent urinary tract infections. 19.6 Summary. 20 Defining evidence in tstatistics. 20.1 Example. 20.2 Evidence in the Student tstatistic. 20.3 The Key Inferential Function for Student's model. 20.4 Corrected evidence. 20.5 A confidence interval for the standardized effect. 20.6 Comparing evidence in t and ztests. 20.7 Summary. 21 Twosample comparisons. 21.1 Drop in systolic blood pressure. 21.2 Defining the standardized effect. 21.3 Evidence in the Welch statistic. 21.4 Confidence intervals for d. 21.5 Summary. 22 Evidence in the chisquared statistic. 22.1 The noncentral chisquared distribution. 22.2 A vst for the noncentral chisquared statistic. 22.3 Simulation studies. 22.4 Choosing the sample size. 22.5 Evidence for l <i" l0. 22.6 Summary. 23 Evidence in Ftests. 23.1 Variance stabilizing transformations for the noncentral F. 23.2 The evidence distribution. 23.3 The Key Inferential Function. 23.4 The random effects model. 23.5 Summary. 24 Evidence in Cochran's Q for heterogeneity of effects. 24.1 Cochran's Q: the fixed effects model. 24.2 Simulation studies. 24.3 Cochran's Q: the random effects model. 24.4 Summary. 25 Combining evidence from K studies. 25.1 Background and preliminary steps. 25.2 Fixed standardized effects. 25.3 Random transformed effects. 25.4 Example: drop in systolic blood pressure. 25.5 Summary. 26 Correcting for publication bias. 26.1 Publication bias. 26.2 The truncated normal distribution. 26.3 Bias correction based on censoring. 26.4 Summary. 27 Largesample properties of variance stabilizing transformations. 27.1 Existence of the variance stabilizing transformation. 27.2 Tests and effect sizes. 27.3 Power and efficiency. 27.4 Summary. References. Index.
 (source: Nielsen Book Data)
 Publisher's Summary
 "Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence" acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence. The book is comprised of two parts  the handbook, and the theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology. This is a coherent introduction to the statistical concepts required to understand the authors' thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.
(source: Nielsen Book Data)  Supplemental links

Publisher description
Table of contents only
Contributor biographical information
Subjects
Bibliographic information
 Publication date
 2008
 Responsibility
 Elena Kulinskaya, Stephan Morgenthaler, Robert G. Staudte.
 Series
 Wiley series in probability and statistics
 ISBN
 9780470028643
 0470028645