How are dichotomous outcome measures primarily represented in meta-analysis?

Dichotomous outcome measures refer to outcomes that can take on one of two possible values, such as success/failure, yes/no, or presence/absence of a disease. In the context of meta-analysis, when researchers analyze studies that report such outcomes, they often employ statistical techniques that help summarize the effect across studies effectively.

Relative risks and odds ratios are specifically suited for dichotomous outcomes. When examining how an intervention impacts the likelihood of an event occurring (e.g., recovery from a disease), these measures provide a comparative perspective, allowing researchers to quantify the strength and direction of the effect between the experimental group and the control group.

Calculating relative risks involves comparing the probability of an outcome in both groups, while odds ratios focus on the odds of the outcome happening in one group relative to the other. Collectively, these measures offer clear insights into the impact of different interventions, making them vital tools in synthesizing findings from various studies with binary outcomes. This is why relative risks or odds ratios are the primary representation method for dichotomous outcome measures in a meta-analysis.