ALL-IN meta-analysis was developed and first applied during the COVID-19 pandemic. While this setting inspired its name, ALL-IN can also benefit non-pandemic circumstances. Conventional meta-analysis loses its coverage when updated repeatedly over time and when the decisions to initiate new trials and synthesize them depend on the results within the meta-analysis (accumulation bias). ALL-IN meta-analysis is anytime-valid. In its simplest form, ALL-IN meta-analysis stays familiar to run and read based on forest plots with confidence intervals that are wider than standard ones. Within collaborative prospective meta-analysis, the payoff is flexibility and speed, with fast sharing of individual participant data or harmonized aggregate data. Outside of this setting, the payoff is validity, when the decisions that shape the evidence (when to stop trials and whether new ones start) are typically outside the control of the meta-analyst. On the one hand, ALL-IN meta-analysis becomes inefficient when there is a maximum sample size or a stopping rule that the meta-analyist can control. On the other hand, it enables adaptations for any evidence synthesis to halfway become living, prospective or even real-time on interim trial results, without complicating the statistics.

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