Data extraction in meta-analysis

Extracting data for meta-analysis can be very frustrating because authors often don’t report the summary data that you want, that is, the same statistics and the right statistics for the meta-analysis software e.g. mean and standard deviation. There are some great resources for data extraction to help you convert data from what’s reported into what you want, but perhaps randomised trials are better served (for example, by the excellent Cochrane Handbook) than other study designs. There are other resources but they’re scattered around and are sometimes not accessible to all those who may want to carry out meta-analysis, as some methods involve complicated equations.

The aim of this resource is to provide a series of useful tips on data extraction, to shed light on, and raise awareness of the different methods and equations that are available to convert data into what you need for meta-analysis. I’ll also try to demystify the maths by giving worked examples and only offering the derivation of the equations as an optional extra.

The data that you will be looking to extract, to input into meta-analysis software, will depend on the outcome and these data will typically be:

This resource is a directory of tips arranged into four groups. Many of these tips have been summarised in a 4-part series of articles about extracting diagnostic accuracy data, extracting categorical risk data, combining and converting groups (relating to continuous data) and summarising good practice guidelines for meta-analysis.

DA1 – The 2x2 table in a diagnostic accuracy study is not given

DA2 – Prevalence is also not reported

DA3 – Sensitivity or specificity is also not reported

DA4 – Neither sensitivity nor specificity is reported