![]() ![]() When this estimated variability in true effects is large, smaller studies are given proportionately more weight than they are in the fixed-effects meta-analysis. This is because under an assumption of common effects, this weighting results in the most precise estimate of the common effect.Ĭonversely, in a random-effects meta-analysis, the weights are also a function of the estimated value. If there is one very large contributing study, the small studies will be given very little weight. ![]() In a fixed-effects analysis, the study estimates are weighted purely according to their estimated variances. My personal view is that this decision ought to be made on the basis of knowledge about the constituent studies, rather than on the basis of actually looking at the point estimates (i.e. Which approach we use affects both the estimated overall effect we obtain and its corresponding 95% confidence interval, and so it is important to decide which is appropriate to use in any given situation. In a random-effects meta-analysis, the observed heterogeneity in the estimates is attributed to two sources: 1) between-study heterogeneity in true effects, and 2) within-study sampling error.įixed versus random-effects meta-analysis Interest then lies in estimating the mean and variance of these true effect sizes across the population of potential studies. In contrast, in a random-effects meta-analysis, we assume that each study is estimating a study-specific true effect (note the lack of a hat here – these are the true effects, not the estimated effects). In the fixed-effects approach, the different effect estimates are attributed purely to random sampling error. In some settings this assumption might be plausible – for example if the studies have all been conducted in the same population, they have used the same inclusion criteria, the treatments have been given in the same way, and outcomes have been measured consistently. In a fixed-effects meta-analysis, we assume that each of the studies included are estimating the same underlying parameter. Along with the estimated effect, we also need the estimated variance or standard error. Examples of possible effect measures are a difference between means between two treatment groups or a log odds ratio from logistic regression fits comparing to exposure groups. We’ll assume that we have estimates from n studies of our effect of interest. In this post we’ll look at some of the consequences of this choice, when in truth the studies are measuring different effects. A choice which has to be made when conducting a meta-analysis is between fixed-effects and random-effects. The essential idea is that the estimates of the effect of interest from previous study are pooled together. It is commonly used within medical and clinical settings to evaluate the existing evidence regarding the effect of a treatment or exposure on an outcome of interest. Meta-analysis is a critical tool for synthesizing existing evidence. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |