Analysing Data and Undertaking Meta‐Analyses

Chapter 10 Analysing data and undertaking meta-analyses Jonathan J Deeks, Jonathan J DeeksSearch for more papers by this authorJulian PT Higgins, Julian PT HigginsSearch for more papers by this authorDouglas G Altman, Douglas G AltmanSearch for more papers by this authoron behalf of the Cochrane Statistical Methods Group, on behalf of the Cochrane Statistical Methods GroupSearch for more papers by this author Jonathan J Deeks, Jonathan J DeeksSearch for more papers by this authorJulian PT Higgins, Julian PT HigginsSearch for more papers by this authorDouglas G Altman, Douglas G AltmanSearch for more papers by this authoron behalf of the Cochrane Statistical Methods Group, on behalf of the Cochrane Statistical Methods GroupSearch for more papers by this author Book Editor(s):Julian P.T. Higgins, Julian P.T. HigginsSearch for more papers by this authorJames Thomas, James ThomasSearch for more papers by this authorJacqueline Chandler, Jacqueline ChandlerSearch for more papers by this authorMiranda Cumpston, Miranda CumpstonSearch for more papers by this authorTianjing Li, Tianjing LiSearch for more papers by this authorMatthew J. Page, Matthew J. PageSearch for more papers by this authorVivian A. Welch, Vivian A. WelchSearch for more papers by this author First published: 20 September 2019 https://doi.org/10.1002/9781119536604.ch10Citations: 78 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Summary This chapter describes the principles and methods used to carry out a meta-analysis for a comparison of two interventions for the main types of data encountered. A very common and simple version of the meta-analysis procedure is commonly referred to as the inverse-variance method. This approach is implemented in its most basic form in RevMan, and is used behind the scenes in many meta-analyses of both dichotomous and continuous data. Results may be expressed as count data when each participant may experience an event, and may experience it more than once. Count data may be analysed using methods for dichotomous data if the counts are dichotomized for each individual, continuous data and time-to-event data, as well as being analysed as rate data. Prediction intervals from random-effects meta-analyses are a useful device for presenting the extent of between-study variation. Sensitivity analyses should be used to examine whether overall findings are robust to potentially influential decisions. Citing Literature Cochrane Handbook for Systematic Reviews of Interventions, Second Edition RelatedInformation