What is meta-analysis in psychology?
There are many meta-analyses in psychology and medicine, areas where studies find often conflicting results. A meta-analysis takes the results from all published studies on the same question and combines them; it’s as if someone had done a single study with a much larger sample size.
What is tau squared in meta-analysis?
In common with other meta-analysis software, RevMan presents an estimate of the between-study variance in a random-effects meta-analysis (known as tau-squared (τ2 or Tau2)). The square root of this number (i.e. tau) is the estimated standard deviation of underlying effects across studies.
What is meta-analysis in clinical research?
Meta-analysis is the process of combining study results that can be used to draw conclusions about therapeutic effectiveness or to plan new studies. The statistical issues include consistency (homogeneity) of study outcomes, and techniques for pooling results from several studies.
What is inverse-variance method?
In statistics, inverse-variance weighting is a method of aggregating two or more random variables to minimize the variance of the weighted average. Each random variable is weighted in inverse proportion to its variance, i.e. proportional to its precision.
How is meta analysis useful to practitioners and scholars?
Meta-Analysis “Increases” Sample Size When individual research projects don’t study a significant number of subjects, it can be difficult to draw reliable and valid conclusions. Meta-studies help overcome the issue of small sample sizes because they review multiple studies across the same subject area.
Why do researchers use meta analysis?
Meta-analyses are conducted to assess the strength of evidence present on a disease and treatment. One aim is to determine whether an effect exists; another aim is to determine whether the effect is positive or negative and, ideally, to obtain a single summary estimate of the effect.
What is heterogeneity in meta-analysis?
Heterogeneity in meta-analysis refers to the variation in study outcomes between studies. The I² statistic describes the percentage of variation across studies that is due to heterogeneity rather than chance (Higgins and Thompson, 2002; Higgins et al., 2003).
Is heterogeneity good in meta-analysis?
When heterogeneity is very high and between-study variation dominates, random-effects meta-analyses weight studies nearly equally, regardless of sample sizes, yielding a meta-analytic summary close to the more easily calculated arithmetic mean of the individual study results.
What is a weighted mean difference?
A (weighted) mean difference is the difference between effect estimates for intervention and control on a specific scale. Assuming this is the study you are referring to this, your scale is time measured in minutes.
What is weighting in meta-analysis?
In meta-analysis, a weighted average effect size is usually obtained to summarize the global magnitude through a set of primary studies. The optimal weight to obtain the unbiased and minimum variance estimator is the inverse variance of each effect-size estimate.
What is the DerSimonian and Laird method?
A variation on the inverse-variance method is to incorporate an assumption that the different studies are estimating different, yet related, intervention effects. This produces a random-effects meta-analysis, and the simplest version is known as the DerSimonian and Laird method(DerSimonian 1986).
Are DerSimonian and Laird estimates of treatment effect unbiased?
In Section 2, the random effects model is described and we also provide a proof that estimates of treatment effect are unbiased under the assumptions of the model. In Section 3 the asymptotic (large number of studies) efficiency of the DerSimonian and Laird estimates is investigated and the small sample case is considered in Section 4.
How does the DerSimonian and Laird procedure for random effects meta-analysis compare?
How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts? The procedure suggested by DerSimonian and Laird is the simplest and most commonly used method for fitting the random effects model for meta-analysis.