The risk of bias arising from incomplete outcome data depends on several factors, including the amount and distribution across intervention groups, the reasons for outcomes being missing, the likely difference in outcome between participants with and without data, what study authors have done to address the problem in their reported analyses, and the clinical context. Therefore it is not possible to formulate a simple rule for judging a study to be at low or high risk of bias. The following considerations may help review authors assess whether incomplete outcome data could be addressed in a way that protects against bias, when using the Collaboration’s tool (Section 8.5).

 

It is often assumed that a high proportion of missing outcomes, or a large difference in proportions between intervention groups, is the main cause for concern over bias. However, these characteristics on their own are insufficient to introduce bias. Here we elaborate on situations in which an analysis can be judged to be at low or high risk of bias. It is essential to consider the reasons for outcomes being missing as well as the numbers missing.