  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.
