Key statistical assumptions and methods in one-arm meta-analyses with binary endpoints and low event rates, including a real-life example in the area of endoscopic colonic stenting

Abstract

There are relatively few publications on the methodology of one-arm meta-analyses, especially when the outcome is binary and has low probability of occurring. We will discuss a few of the important assumptions underlying one-arm meta-analyses, including publication bias, fixed effect versus random-effects models, and raw event incidence rate transformations required when the event frequency is low. Finally, we will provide a real-life example taken from the endoscopic colonic stenting literature to illustrate the consequences of failure to thoroughly investigate these assumptions. In this example, we find arcsine transformation provides more appropriate results than logit transformation.

Keywords

• one-arm meta-analysis
• low event rates
• publication bias
• random-effects model
• colonic stenting
• endoscopy