CHOOSING EFFECT MEASURES AND COMPUTING ESTIMATES OF EFFECT

Higgins et al. (2023) consider four critical points in this regard:

a. As review authors, researchers will likely encounter various outcome data types in your work. These include dichotomous, continuous, ordinal, count or rate, and time-to-event data. By familiarising these types, you can enhance your understanding of the research process and feel more empowered.

b. When comparing outcome data between two intervention groups ('effect measures'), there are many methods for each data type. Comparisons of binary outcomes can utilise a risk ratio, an odds ratio, a risk difference, or a number needed to treat. Continuous outcomes, on the other hand, can be compared using a mean difference or a standardised mean difference. This diversity of methods enriches researchers' comprehension of the research process.

c. Effect measures come in two types: ratio measures (risk ratio and odds ratio) or difference measures (such as mean difference and risk difference). Ratio measures are usually analysed using a logarithmic scale.

d. Information obtained from research reports might require conversion into a consistent or usable format for analysis.