8/19/2023 0 Comments Definition of meta![]() ![]() Study weights will need to consider both sources of variance, and the single-value pooled estimate can only be regarded as the mean of a distribution of effects and not as a true effect for any real population. ![]() Sampling error will still contribute to explain deviations between study-specific estimates and the assumed “true” effect for each particular study. a degree of between-study variability beyond what is expected to occur by chance. In this manner, deviations of individual studies from the center of such distribution represent true heterogeneity, i.e. sampling error), and the pooled estimate is interpreted as the best estimate of the common underlying effect.Īs opposed to this, a random effects meta-analysis assumes the existence of a distribution of true effects applicable to a set of different studies and populations. As a consequence, the study weights are calculated taking into account only the within-study variance (i.e. ![]() Thus, deviations of individual studies from this true effect represent only random variation due to sampling error. Ī fixed effects meta-analysis assumes that a single “true” effect exists, which is common to all observed studies. However, the manner in which the above mentioned weights are calculated and the weighted mean is interpreted differs substantially according to the assumed nature of the sources of heterogeneity. The output of a meta-analysis is typically a single-value pooled estimate of effect, along with its standard error, which is calculated as a weighted mean of individual studies where the weights are the inverse of the variance of the study-level parameter estimates. From a broader perspective, meta-analysis and meta-regression are part of a systematic, integrative process to make sense of publicly available yet disperse, imprecise, and heterogeneous information. Meta-analysis can be regarded as a set of statistical tools to combine and summarize the results of multiple individual epidemiological studies. This summary focuses on methods applicable to meta-regression of absolute and relative measures of association derived from 2×2 tables (risk difference, odds ratio, risk ratio), or meta-regression of continuous variable outcomes, where only aggregated data are available (no meta-analysis or pooled analysis of individual data).ĭistinction between fixed and random effects: ![]() The latter acquires special importance when conducting meta-regression. data available on the individual level, study-level summary counts for the cells of 2×2 tables, or one effect measure per study plus a variance or standard error), the nature of the measure of effect (relative measures of association, absolute measures of association, means, correlations, proportions, including diagnostic performance statistics, p-values, etc.), and the assumed nature of the variability observed across studies (fixed effects vs. There exist different methods for meta-analysis and meta-regression to accommodate the varied manners in which data can be presented (i.e. For brevity, we will assume that a properly designed and conducted systematic review and meta-analysis have been already performed, so we will focus on the meta-regression techniques only. This next step in the integrative methodology may help to better understand whether and which study-level factors drive the measures of effect. However, when there is substantial unaccounted heterogeneity in the outcome of interest across studies, it may be relevant to continue investigating whether such heterogeneity may be further explained by differences in characteristics of the studies (methodological diversity) or study populations (clinical diversity). Often times, a systematic review of literature stops after obtaining a meta-analytic aggregate measure of the parameter(s) of interest. Meta-regression is a statistical method that can be implemented following a traditional meta-analysis and can be regarded as an extension to it. ![]()
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