One is to test the null hypothesis of no effect and report a p-value. In primary studies that compare outcomes in two groups, there are two general approaches that researchers apply.
#Comprehensive meta analysis video how to#
I show how to perform the analysis and report the results. It provides experience working with a difference in means where the effect size is consistent across studies. This is an analysis of randomized controlled trials (RCTs) that compared the duration of flu symptoms in patients treated with Tamiflu to those treated with a placebo. Tamiflu is a drug that is widely prescribed for treatment of flu symptoms. We discuss how to choose an effect size index, and how to understand and explain the meaning of the effect size. In this module, I discuss some of the common effect-size indices. Then, we pool these values to estimate the common (or mean) effect size, we estimate the dispersion in effects, and we sometimes try to explain the variation in effects. We compute an effect size for each study. The effect size is the unit of currency in a meta-analysis. We discuss how to select a model, and also how to avoid common mistakes related to this issue. The random-effects model applies when our goal is to generalize from these studies to a wider universe of comparable studies or populations. The fixed-effect model applies when our goal is to estimate the common effect (or the mean effect) for the studies actually included in the analysis. There is a widespread belief that the fixed-effect model applies when the true effect size is the same in all populations, and the random-effects model applies otherwise. The model tells us how the studies were sampled and how we can generalize from them to other studies or populations. Every meta-analysis must be based on a statistical model. When the effect size varies across studies we estimate the mean effect size, but we also need to estimate the dispersion in effects and consider the implications of this dispersion for the utility of the intervention. When the effect size is consistent across studies, we focus on the common effect size.
#Comprehensive meta analysis video series#
In this module I use a series of fictional studies to show what happens as we add studies to a meta-analysis. I also use this to show how we can perform a simple analysis from start to finish, including generating a high-resolution plot and writing a report. I use this to outline the elements of a meta-analysis that we will be exploring in later modules. We start with a simple meta-analysis to compare the relative utility of two treatments for preventing cardiovascular events. These contain information that is important for the modules that follow. Modules marked with an asterisk ( *) should not be skipped.
Understand the common misinterpretation of the I-squared statistic.Understand limitations of the random-effects analysis.
Understand limitations of the fixed-effect analysis.Avoid mistakes in choosing a statistical model.How to write a report that explains the results of the analysis How to understand the difference between a confidence interval and a prediction interval.How to plot the distribution of true effects.What I-squared tells us (and does not tell us).How to interpret each of the statistics associated with heterogeneity.To see how much weight is assigned to each study.How to use the program Comprehensive Meta-Analysis broad inclusion criteria for selecting studies How we can (and cannot) generalize from the meta-analysis to other populations.At the conclusion of this course, you will understand:
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