During the past 30 years, meta-analysis has been an indispensable tool for revealing the hidden meaning of our research literatures. The four articles in this special section on meta-analysis illus...

Meta-Analysis: A Constantly Evolving Research Integration Tool

The rapid technological developments of the past decade and the changes in echocardiographic practice brought about by these developments have resulted in the need for updated recommendations to the previously published guidelines for cardiac chamber quantification, which was the goal of the joint writing group assembled by the American Society of Echocardiography and the European Association of Cardiovascular Imaging. This document provides updated normal values for all four cardiac chambers, including three-dimensional echocardiography and myocardial deformation, when possible, on the basis of considerably larger numbers of normal subjects, compiled from multiple databases. In addition, this document attempts to eliminate several minor discrepancies that existed between previously published guidelines.

https://biblio.ugent.be/publication/5953422/file/5953443.pdf

Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging.

The metafor package provides functions for conducting meta-analyses in R. The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. Meta-regression analyses with continuous and categorical moderators can be conducted in this way. Functions for the Mantel-Haenszel and Peto's one-step method for meta-analyses of 2 x 2 table data are also available. Finally, the package provides various plot functions (for example, for forest, funnel, and radial plots) and functions for assessing the model fit, for obtaining case diagnostics, and for tests of publication bias.

Conducting Meta-Analyses in R with the metafor Package

The present article presents a meta-analytic test of intergroup contact theory. With 713 independent samples from 515 studies, the meta-analysis finds that intergroup contact typically reduces intergroup prejudice. Multiple tests indicate that this finding appears not to result from either participant selection or publication biases, and the more rigorous studies yield larger mean effects. These contact effects typically generalize to the entire outgroup, and they emerge across a broad range of outgroup targets and contact settings. Similar patterns also emerge for samples with racial or ethnic targets and samples with other targets. This result suggests that contact theory, devised originally for racial and ethnic encounters, can be extended to other groups. A global indicator of Allport's optimal contact conditions demonstrates that contact under these conditions typically leads to even greater reduction in prejudice. Closer examination demonstrates that these conditions are best conceptualized as an interrelated bundle rather than as independent factors. Further, the meta-analytic findings indicate that these conditions are not essential for prejudice reduction. Hence, future work should focus on negative factors that prevent intergroup contact from diminishing prejudice as well as the development of a more comprehensive theory of intergroup contact.

A meta-analytic test of intergroup contact theory.

This article presents findings from a meta-analysis of 213 school-based, universal social and emotional learning (SEL) programs involving 270,034 kindergarten through high school students. Compared to controls, SEL participants demonstrated significantly improved social and emotional skills, attitudes, behavior, and academic performance that reflected an 11-percentile-point gain in achievement. School teaching staff successfully conducted SEL programs. The use of 4 recommended practices for developing skills and the presence of implementation problems moderated program outcomes. The findings add to the growing empirical evidence regarding the positive impact of SEL programs. Policy makers, educators, and the public can contribute to healthy development of children by supporting the incorporation of evidence-based SEL programming into standard educational practice.

http://explorefhp.com/wp-content/uploads/2013/03/Durlak-Weissberg-Child-Development-Meta-Analysis.pdf

The Impact of Enhancing Students’ Social and Emotional Learning: A Meta‐Analysis of School‐Based Universal Interventions

/pdf/statistical-methods-for-meta-analysis-33nq4e8ph2.pdf

Statistical Methods for Meta‐Analysis

Background The relation between abnormalities in the lumbar spine and low back pain is controversial. We examined the prevalence of abnormal findings on magnetic resonance imaging (MRI) scans of the lumbar spine in people without back pain. Methods We performed MRI examinations on 98 asymptomatic people. The scans were read independently by two neuroradiologists who did not know the clinical status of the subjects. To reduce the possibility of bias in interpreting the studies, abnormal MRI scans from 27 people with back pain were mixed randomly with the scans from the asymptomatic people. We used the following standardized terms to classify the five intervertebral disks in the lumbosacral spine: normal, bulge (circumferential symmetric extension of the disk beyond the interspace), protrusion (focal or asymmetric extension of the disk beyond the interspace), and extrusion (more extreme extension of the disk beyond the interspace). Nonintervertebral disk abnormalities, such as facet arthropathy, were also d...

Magnetic resonance imaging of the lumbar spine in people without back pain.

Preface. Acknowledgments. 1. Introduction. 1.1 Why This Book? 1.2 What Is Diagnostic Accuracy? 1.3 Landmarks in Statistical Methods for Diagnostic Medicine. 1.4 Software. 1.5 Topics not Covered in This Book. 1.6 Summary. I BASIC CONCEPTS AND METHODS. 2. Measures of Diagnostic Accuracy. 2.1 Sensitivity and Specificity. 2.2 The Combined Measures of Sensitivity and Specificity. 2.3 The ROC Curve. 2.4 The Area Under the ROC Curve. 2.5 The Sensitivity at a Fixed FPR. 2.6 The Partial Area Under the ROC Curve. 2.7 Likelihood Ratios. 2.8 Other ROC Curve Indices. 2.9 The Localization and Detection of Multiple Abnormalities. 2.10 Interpretation of Diagnostic Tests. 2.11 Optimal Decision Threshold on the ROC Curve. 2.12 Multiple Tests. 3. The Design of Diagnostic Accuracy Studies. 3.1 Determining the Objective of the Study. 3.2 Identifying the Target Patient Population. 3.3 Selecting a Sampling Plan for Patients. 3.3.1 Phase I: Exploratory Studies. 3.3.2 Phase II: Challenge Studies. 3.3.3 Phase III: Clinical Studies. 3.4 Selecting the Gold Standard. 3.5 Choosing a Measure of Accuracy. 3.6 Identifying the Target Reader Population. 3.7 Selecting a Sampling Plan for Readers. 3.8 Planning the Data Collection. 3.8.1 Format for the Test Results. 3.8.2 Data Collection for the Reader Studies. 3.8.3 Reader Training. 3.9 Planning the Data Analyses. 3.9.1 Statistical Hypotheses. 3.9.2 Reporting the Test Results. 3.10 Determining the Sample Size. 4. Estimation and Hypothesis Testing in a Single Sample. 4.1 Binary Scale Data. 4.1.1 Sensitivity and Specificity. 4.1.2 The Sensitivity and Specificity of Clustered Binary Data. 4.1.3 The Likelihood Ratio (LR). 4.1.4 The Odds Ratio. 4.2 Ordinal Scale Data. 4.2.1 The Empirical ROC Curve. 4.2.2 Fitting a Smooth Curve (Parametric Model). 4.2.3 Estimation of Sensitivity at a Particular FPR. 4.2.4 The Area and Partial Area Under the ROC Curve (Parametric Model). 4.2.5 The Area Under the Curve (Nonparametric Method). 4.2.6 Nonparametric Analysis of Clustered Data. 4.2.7 The Degenerate Data. 4.2.8 Choosing Between Parametric and Nonparametric Methods. 4.3 Continuous Scale Data. 4.3.1 The Empirical ROC Curve. 4.3.2 Fitting a Smooth ROC Curve (Parametric and Nonparametric Methods). 4.3.3 Area Under the ROC Curve (Parametric and Nonparametric). 4.3.4 Fixed FPR The Sensitivity and Decision Threshold. 4.3.5 Choosing the Optimal Operating Point. 4.3.6 Choosing Between Parametric and Nonparametric Techniques. 4.4 Hypothesis Testing About the ROC Area. 5. Comparing the Accuracy of Two Diagnostic Tests. 5.1 Binary Scale Data. 5.1.1 Sensitivity and Specificity. 5.1.2 Sensitivity and Specificity of Clustered Binary Data. 5.2 Ordinal and Continuous Scale Data. 5.2.1 Determining the Equality of Two ROC Curves. 5.2.2 Comparing ROC Curves at a Particular Point. 5.2.3 Determining the Range of FPR for Which TPR Differ. 5.2.4 A Comparison of the Area or Partial Area. 5.3 Tests of Equivalence. 6. Sample Size Calculation. 6.1 The Sample Size for Accuracy Studies of a Single Test. 6.1.1 Sensitivity and Specificity. 6.1.2 The Area Under the ROC Curve. 6.1.3 The Sensitivity at a Fixed FPR. 6.1.4 The Partial Area Under the ROC Curve. 6.2 The Sample Size for the Accuracy of Two Tests. 6.2.1 Sensitivity and Specificity. 6.2.2 The Area Under the ROC Curve. 6.2.3 The Sensitivity at a Fixed FPR. 6.2.4 The Partial Area Under the ROC Curve. 6.3 The Sample Size for Equivalent Studies of Two Tests. 6.4 The Sample Size for Determining a Suitable Cutoff Value. 7. Issues in Meta Analysis for Diagnostic Tests. 7.1 Objectives. 7.2 Retrieval of the Literature. 7.3 Inclusion Exclusion Criteria. 7.4 Extracting Information From the Literature. 7.5 Statistical Analysis. 7.6 Public Presentation. II ADVANCED METHODS. 8. Regression Analysis for Independent ROC Data. 8.1 Four Clinical Studies. 8.1.1 Surgical Lesion in a Carotid Vessel Example. 8.1.2 Pancreatic Cancer Exampl. 8.1.3 Adult Obesity Example. 8.1.4 Staging of Prostate Cancer Example. 8.2 Regression Models for Continuous Scale Tests. 8.2.1 Indirect Regression Models for Smooth ROC Curves. 8.2.2 Direct Regression Models for Smooth ROC Curves. 8.2.3 MRA Use for Surgical Lesion Detection in the Carotid Vessel. 8.2.4 Biomarkers for the Detection of Pancreatic Cancer. 8.2.5 Prediction of Adult Obesity by Using Childhood BMI Measurements. 8.3 Regression Models for Ordinal Scale Tests. 8.3.1 Indirect Regression Models for Latent Smooth ROC Curves. 8.3.2 Direct Regression Model for Latent Smooth ROC Curves. 8.3.3 Detection of Periprostatic Invasion With US. 9. Analysis of Correlated ROC Data. 9.1 Studies With Multiple Test Measurements of the Same Patient. 9.1.1 Indirect Regression Models for Ordinal Scale Tests. 9.1.2 Neonatal Examination Example. 9.1.3 Direct Regression Models for Continuous Scale Tests. 9.2 Studies With Multiple Readers and Tests. 9.2.1 A Mixed Effects ANOVA Model for Summary Measures of Diagnostic Accuracy. 9.2.2 Detection of TAD Example. 9.2.3 The Mixed Effects ANOVA Model for Jackknife Pseudovalues. 9.2.4 Neonatal Examination Example. 9.2.5 A Bootstrap Method. 9.3 Sample Size Calculation for Multireader Studies. 10. Methods for Correcting Verification Bias. 10.1 A Single Binary Scale Test. 10.1.1 Correction Methods With the MAR Assumption. 10.1.2 Correction Methods Without the MAR Assumption. 10.1.3 Hepatic Scintigraph Example. 10.2 Correlated Binary Scale Tests. 10.2.1 An ML Approach Without Covariates. 10.2.2 An ML Approach With Covariates. 10.2.3 Screening Tests for Dementia Disorder Example. 10.3 A Single Ordinal Scale Test. 10.3.1 An ML Approach Without Covariates. 10.3.2 Fever of Uncertain Origin Example. 10.3.3 An ML Approach With Covariates. 10.3.4 Screening Test for Dementia Disorder Example. 10.4 Correlated Ordinal Scale Tests. 10.4.1 The Weighted GEE Approach for Latent Smooth ROC Curves. 10.4.2 A Likelihood Based Approach for ROC Areas. 10.4.3 Use of CT and MRI for Staging Pancreatic Cancer Example. 11. Methods for Correcting Imperfect Standard Bias. 11.1 One Single Test in a Single Population. 11.1.1 Hypothetical and Strongyloides Infection Examples. 11.2 One Single Test in G Populations. 11.2.1 Tuberculosis Example. 11.3 Multiple Tests in One Single Population. 11.3.1 MLEs Under the CIA. 11.3.2 Assessment of Pleural Thickening Example. 11.3.3 ML Approaches Without the CIA. 11.3.4 Bioassays for HIV Example. 11.4 Multiple Binary Tests in G Populations. 11.4.1 ML Approaches Under the CIA. 11.4.2 ML Approaches Without the CIA. 12. Statistical Methods for Meta Analysis. 12.1 Sensitivity and Specificity Pairs. 12.1.1 One Common SROC Curve. 12.1.2 Study Specific SROC Curve. 12.1.3 Evaluation of Duplex Ultrasonography, With and Without Color Guidance. 12.2 ROC Curve Areas. 12.2.1 Fixed Effects Models. 12.2.2 Random Effects Models. 12.2.3 Evaluation of the Dexamethasone Suppression.Test. Index.

Statistical Methods in Diagnostic Medicine

Sensitivity and specificity are the basic measures of accuracy of a diagnostic test; however, they depend on the cut point used to define “positive” and “negative” test results. As the cut point shifts, sensitivity and specificity shift. The receiver operating characteristic (ROC) curve is a plot of the sensitivity of a test versus its false-positive rate for all possible cut points. The advantages of the ROC curve as a means of defining the accuracy of a test, construction of the ROC, and identification of the optimal cut point on the ROC curve are discussed. Several summary measures of the accuracy of a test, including the commonly used percentage of correct diagnoses and area under the ROC curve, are described and compared. Two examples of ROC curve application in radiologic research are presented. © RSNA, 2003

Receiver Operating Characteristic Curves and Their Use in Radiology

Current methods for estimating the accuracy of diagnostic tests require independence of the test results in the sample. However, cases in which there are multiple test results from the same patient are quite common. In such cases, estimation and inference of the accuracy of diagnostic tests must account for intracluster correlation. In the present paper, the structural components method of DeLong, DeLong, and Clarke-Pearson (1988, Biometrics 44, 837-844) is extended to the estimation of the Receiver Operating Characteristics (ROC) curve area for clustered data, incorporating the concepts of design effect and effective sample size used by Rao and Scott (1992, Biometrics 48, 577-585) for clustered binary data. Results of a Monte Carlo simulation study indicate that the size of statistical tests that assume independence is inflated in the presence of intracluster correlation. The proposed method, on the other hand, appropriately handles a wide variety of intracluster correlations, e.g., correlations between true disease statuses and between test results. In addition, the method can be applied to both continuous and ordinal test results. A strategy for estimating sample size requirements for future studies using clustered data is discussed.

Nancy A. Obuchowski

Papers

Statistical Methods for Meta‐Analysis

Magnetic resonance imaging of the lumbar spine in people without back pain.

Statistical Methods in Diagnostic Medicine

Receiver Operating Characteristic Curves and Their Use in Radiology

Nonparametric analysis of clustered ROC curve data.