Science is idolized as a cumulative process ("standing on the shoulders of giants"), yet scientific knowledge is typically built on a patchwork of research contributions without much coordination. This lack of efficiency has specifically been addressed in clinical research by recommendations for living systematic reviews and against research waste. We propose to further those recommendations with ALL-IN meta-analysis: Anytime Live and Leading INterim meta-analysis. ALL-IN provides statistical methodology for a meta-analysis that can be updated at any time – reanalyzing after each new observation while retaining type-I error guarantees, live – no need to prespecify the looks, and leading – in the decisions on whether individual studies should be initiated, stopped or expanded, the meta-analysis can be the leading source of information. We illustrate the method for time-to-event data, showing how synthesizing data at interim stages of studies can increase efficiency when studies are slow in themselves to provide the necessary number of events for completion. The meta-analysis can be performed on interim data, but does not have to. The analysis design requires no information about the number of patients in trials or the number of trials eventually included. So it can breathe life into living systematic reviews, through better and simpler statistics, efficiency, collaboration and communication.

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Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning
Machine Learning

ter Schure, J.A, & Grünwald, P.D. (2021). ALL-IN meta-analysis: Breathing life into living systematic reviews.