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There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd.

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When we speak about heterogeneity in a meta-analysis, our intent is usually to understand the substantive implications of the heterogeneity. If an intervention yields a mean effect size of 50 points, we want to know if the effect size in different populations varies from 40 to 60, or from 10 to 90, because this speaks to the potential utility of the intervention. While there is a common belief that the I statistic provides this information, it actually does not. In this example, if we are told that I is 50%, we have no way of knowing if the effects range from 40 to 60, or from 10 to 90, or across some other range. Rather, if we want to communicate the predicted range of effects, then we should simply report this range. This gives readers the information they think is being captured by I and does so in a way that is concise and unambiguous. Copyright © 2017 John Wiley & Sons, Ltd.

Publication bias is the term for what occurs whenever the research that appears in the published literature is systematically unrepresentative of the population of completed studies. Simply put, when the research that is readily available differs in its results from the results of all the research that has been done in an area, readers and reviewers of that research are in danger of drawing the wrong conclusion about what that body of research shows. In some cases this can have dramatic consequences, as when an ineffective or dangerous treatment is falsely viewed as safe and effective. This can be illustrated by two events that received much media attention as this book was going to press in late 2004. These are the debate surrounding Merck's recall of Vioxx, a popular arthritis drug (Merck maintained that it recalled Vioxx as soon as the data indicated the high prevalence of cardiovascular events among those who took Vioxx for more than 18 months, while media reports said that Merck hid adverse event evidence for years), and the use of selective serotonin reuptake inhibitor (SSRI) anti-depressants among adolescents (Elliott Spitzer, attorney general of New York State, filed a 2004 lawsuit against GlaxoSmithKline,