A selection of studies in which these characteristics differ can allow investigation of the consistency of effect across a wider range of populations and interventions. Primary studies often involve a specific type of participant and explicitly defined interventions. To answer questions not posed by the individual studies.Estimation is usually improved when it is based on more information.
Many studies are too small to provide convincing evidence about intervention effects in isolation. Potential advantages of meta-analyses include the following: Such a meta-analysis yields an overall statistic (together with its confidence interval) that summarizes the effectiveness of an experimental intervention compared with a comparator intervention. 10.2 Introduction to meta-analysisĪn important step in a systematic review is the thoughtful consideration of whether it is appropriate to combine the numerical results of all, or perhaps some, of the studies. Review authors should consult the chapters that precede this one before a meta-analysis is undertaken. The production of a diamond at the bottom of a plot is an exciting moment for many authors, but results of meta-analyses can be very misleading if suitable attention has not been given to formulating the review question specifying eligibility criteria identifying and selecting studies collecting appropriate data considering risk of bias planning intervention comparisons and deciding what data would be meaningful to analyse. It can be tempting to jump prematurely into a statistical analysis when undertaking a systematic review. Cochrane Handbook for Systematic Reviews of Interventions version 6.2 (updated February 2021). In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Chapter 10: Analysing data and undertaking meta-analyses. Sensitivity analyses should be used to examine whether overall findings are robust to potentially influential decisions.Ĭite this chapter as: Deeks JJ, Higgins JPT, Altman DG (editors).
Many judgements are required in the process of preparing a meta-analysis. Prediction intervals from random-effects meta-analyses are a useful device for presenting the extent of between-study variation. Random-effects meta-analyses allow for heterogeneity by assuming that underlying effects follow a normal distribution, but they must be interpreted carefully. Variation across studies (heterogeneity) must be considered, although most Cochrane Reviews do not have enough studies to allow for the reliable investigation of its causes. For rare events, the Peto method has been observed to be less biased and more powerful than other methods. Studies with no events contribute no information about the risk ratio or odds ratio.
Most meta-analysis methods are variations on a weighted average of the effect estimates from the different studies. dichotomous, continuous) that result from measurement of an outcome in an individual study, and to choose suitable effect measures for comparing intervention groups. It is important to be familiar with the type of data (e.g. However, they also have the potential to mislead seriously, particularly if specific study designs, within-study biases, variation across studies, and reporting biases are not carefully considered. Potential advantages of meta-analyses include an improvement in precision, the ability to answer questions not posed by individual studies, and the opportunity to settle controversies arising from conflicting claims. Meta-analysis is the statistical combination of results from two or more separate studies.
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