M. Borenstein, L. Hedges, J. Higgins, H. Rothstein
{"title":"How a Meta‐Analysis Works","authors":"M. Borenstein, L. Hedges, J. Higgins, H. Rothstein","doi":"10.1002/9780470743386.CH1","DOIUrl":"https://doi.org/10.1002/9780470743386.CH1","url":null,"abstract":"Figure 1.1 illustrates a meta-analysis that shows the impact of high dose versus standard dose of statins in preventing death and myocardial infarction (MI). This analysis is adapted from one reported by Cannon et al. and published in the Journal of the American College of Cardiology (2006). Our goal in presenting this here is to introduce the various elements in a meta-analysis (the effect size for each study, the weight assigned to each effect size, the estimate of the summary effect, and so on) and show where each fits into the larger scheme. In the chapters that follow, each of these elements will be explored in detail.","PeriodicalId":105695,"journal":{"name":"Introduction to Meta‐Analysis","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127688012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Borenstein, L. Hedges, J. Higgins, H. Rothstein
{"title":"Further Methods for Dichotomous Data","authors":"M. Borenstein, L. Hedges, J. Higgins, H. Rothstein","doi":"10.1002/9780470743386.CH37","DOIUrl":"https://doi.org/10.1002/9780470743386.CH37","url":null,"abstract":"","PeriodicalId":105695,"journal":{"name":"Introduction to Meta‐Analysis","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130755534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Borenstein, L. Hedges, J. Higgins, H. Rothstein
{"title":"Reporting the Results of a Meta‐Analysis","authors":"M. Borenstein, L. Hedges, J. Higgins, H. Rothstein","doi":"10.1002/9780470743386.CH41","DOIUrl":"https://doi.org/10.1002/9780470743386.CH41","url":null,"abstract":"","PeriodicalId":105695,"journal":{"name":"Introduction to Meta‐Analysis","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127023910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Borenstein, L. Hedges, J. Higgins, H. Rothstein
{"title":"Independent Subgroups within a Study","authors":"M. Borenstein, L. Hedges, J. Higgins, H. Rothstein","doi":"10.1002/9780470743386.CH23","DOIUrl":"https://doi.org/10.1002/9780470743386.CH23","url":null,"abstract":"","PeriodicalId":105695,"journal":{"name":"Introduction to Meta‐Analysis","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115619081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Borenstein, L. Hedges, J. Higgins, H. Rothstein
{"title":"When Does it Make Sense to Perform a Meta‐Analysis?","authors":"M. Borenstein, L. Hedges, J. Higgins, H. Rothstein","doi":"10.1002/9780470743386.CH40","DOIUrl":"https://doi.org/10.1002/9780470743386.CH40","url":null,"abstract":"In the early days of meta-analysis (at least in its current incarnation) Robert Rosenthal was asked if it makes sense to perform a meta-analysis, given that the studies differ in various ways, and the analysis amounts to combining apples and oranges. Rosenthal answered that combining apples and oranges makes sense if your goal is to produce a fruit salad. The goal of a meta-analysis is only rarely to synthesize data from a set of identical studies. Almost invariably, the goal is to broaden the base of studies in some way, expand the question, and study the pattern of answers. The question of whether it makes sense to perform a meta-analysis, and the question of what kinds of studies to include, must be asked and answered in the context of specific goals. The ability to combine data from different studies to estimate the common effect (or mean effect), continues to be an important function of meta-analysis. However, it is not the only function. The goal of some syntheses will be to report the summary effect, but the goal of other syntheses will be to assess the dispersion as well as the mean effect, and the goal of others will be to focus on the dispersion exclusively. For example, suppose that we are looking at the impact of a teaching intervention on student performance. Does it make sense to include studies that measured verbal skills and also studies that measured math skills? If our goal is to assess the impact on performance in general, then the answer is Yes. If our goal is to assess the impact on verbal skills alone, then the answer is No. Does it make sense to include studies","PeriodicalId":105695,"journal":{"name":"Introduction to Meta‐Analysis","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125625401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Borenstein, L. Hedges, J. Higgins, H. Rothstein
{"title":"Converting Among Effect Sizes","authors":"M. Borenstein, L. Hedges, J. Higgins, H. Rothstein","doi":"10.1002/9780470743386.CH7","DOIUrl":"https://doi.org/10.1002/9780470743386.CH7","url":null,"abstract":"","PeriodicalId":105695,"journal":{"name":"Introduction to Meta‐Analysis","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125464807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Borenstein, L. Hedges, J. Higgins, H. Rothstein
{"title":"Effect Sizes Based on Correlations","authors":"M. Borenstein, L. Hedges, J. Higgins, H. Rothstein","doi":"10.1002/9780470743386.CH6","DOIUrl":"https://doi.org/10.1002/9780470743386.CH6","url":null,"abstract":"where n is the sample size. Most meta-analysts do not perform syntheses on the correlation coefficient itself because the variance depends strongly on the correlation. Rather, the correlation is converted to the Fisher’s z scale (not to be confused with the z-score used with significance tests), and all analyses are performed using the transformed values. The results, such as the summary effect and its confidence interval, would then be converted back to correlations for presentation. This is shown schematically in Figure 6.1, and is analogous to the procedure used with odds ratios or risk ratios where all analyses are performed using log transformed values, and then converted back to the original metric.","PeriodicalId":105695,"journal":{"name":"Introduction to Meta‐Analysis","volume":"181 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133074059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Borenstein, L. Hedges, J. Higgins, Hannah R. Rothstein
{"title":"Simpson's Paradox","authors":"M. Borenstein, L. Hedges, J. Higgins, Hannah R. Rothstein","doi":"10.1002/9780470743386.CH33","DOIUrl":"https://doi.org/10.1002/9780470743386.CH33","url":null,"abstract":"A prominent university was accused of gender discrimination in its graduate admission policies. The difference seemed to be too large to be due just to chance. Here are the numbers: Gender Applicants % Admitted Men 8,442 44% Women 4,321 35% But an examination of the admissions of individual departments revealed no such bias. Instead, there was a small bias in favor of admitting women. Here are the numbers for the main departments: Department Men Women Applicants % Admitted Applicants % Admitted A 825 62% 108 82% B 560 63% 25 68% C 325 37% 593 34% D 417 33% 375 35% E 191 28% 393 24% F 272 6% 341 7% This occured because women were applying to competitive departments with a low rate of admissions, while men were applying to less competitive departments with high rates of admissions.","PeriodicalId":105695,"journal":{"name":"Introduction to Meta‐Analysis","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114139320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Borenstein, L. Hedges, J. Higgins, H. Rothstein
{"title":"Generality of the Basic Inverse‐Variance Method","authors":"M. Borenstein, L. Hedges, J. Higgins, H. Rothstein","doi":"10.1002/9780470743386.CH34","DOIUrl":"https://doi.org/10.1002/9780470743386.CH34","url":null,"abstract":"","PeriodicalId":105695,"journal":{"name":"Introduction to Meta‐Analysis","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122696780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Overview","authors":"T. Panontin","doi":"10.2307/j.ctvq4c011.3","DOIUrl":"https://doi.org/10.2307/j.ctvq4c011.3","url":null,"abstract":"The National Symposium on Fatigue and Fracture Mechanics is a forum for presentation and discussion of significant research and its application to life prediction and structural integrity. For the 29th symposium, nearly 100 researchers from 14 countries gathered at Stanford University in Stanford, California on June 24-26, 1997. There, they exchanged information on recent developments on modeling and analyzing fatigue and fracture processes; on applications to real structures and new materials; and on directions for future research. The symposium was organized toward these goals by a group of leading researchers who work in all aspects of fracture and fatigue. The members of this committee were Robert Dodds, Jr., James Newman, Jr., Drew Nelson, Mark Kirk, James Joyce, Robert Dexter, Michael Mitchell, and Walter Reuter, and the success of the symposium is a direct reflection of their efforts. This Special Technical Publication (STP) documents the technical interchange of the 29th Symposium on Fatigue and Fracture Mechanics. It contains 51 papers, 27 on fracture mechanics and 24 on fatigue. In addition to the fine contributions made directly by the authors of the papers, the quality of the papers is a result of the diligence and commitment of a large number of reviewers. The contributions of the editors at ASTM should also be acknowledged. The first paper in the volume is a synopsis of the Twenty-Ninth National Symposium J.L. Swedlow Lecture by Professor C. Fong Shih. Professor Shih's lecture, entitled \"Fracture Analysis In The Ductile/Brittle Regime: A Predictive Tool Using Cell Models,\" described the current state of the art of two-parameter and mechanism-based fracture prediction approaches, with emphasis placed on the development of computational cell models. Professor Shih showed that, within their respective regimes of applicability, both approaches correctly correlate constraint effects on fracture toughness. The 50 papers that followed in the symposium are organized in this volume in three main categories: Fracture Mechanics, Fatigue, and Structural Applications. These are described below.","PeriodicalId":105695,"journal":{"name":"Introduction to Meta‐Analysis","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121597499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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