Available from 6.1 Types of data and effect measuresĦ.1.1 Types of data Update to this section pendingĪ key early step in analysing results of studies of effectiveness is identifying the data type for the outcome measurements. Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Chapter 6: Choosing effect measures and computing estimates of effect. Results extracted from study reports may need to be converted to a consistent, or usable, format for analysis.Ĭite this chapter as: Higgins JPT, Li T, Deeks JJ (editors). Ratio measures are typically analysed on a logarithmic scale. risk ratio, odds ratio) or difference measures (e.g. Continuous outcomes can be compared between intervention groups using a mean difference or a standardized mean difference.Įffect measures are either ratio measures (e.g. For example, dichotomous outcomes can be compared between intervention groups using a risk ratio, an odds ratio, a risk difference or a number needed to treat. There are several different ways of comparing outcome data between two intervention groups (‘effect measures’) for each data type. The types of outcome data that review authors are likely to encounter are dichotomous data, continuous data, ordinal data, count or rate data and time-to-event data.

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