Nowadays, meta-analyses are typically updated every 1 to 3 years as data from new clinical trials emerge. Repeated updating of meta-analyses force authors to repeatedly employ the same statistical hypothesis testing on accumulating meta-analysis data. This conduct has long been known to inflate the risk of falsely inferring a statistically significant difference between two interventions under investigation. Similarly, repeated statistical hypothesis testing also inflates the risk of inferring no difference exists between two interventions, when the truth is that there is a difference.

In clinical trials where interim analyses (interim hypothesis tests) are performed over the accumulation of patients (a scenario akin to meta-analysis updates over the accumulation of trials), advanced sequential hypothesis testing techniques have long been used to control the risks of false statistical inferences. Empirical studies suggest that such sequential hypothesis testing techniques are equally effective in controlling the risks of false inferences in meta-analyses.