PDF(Pubmed)
Abstract：
Science is justly admired 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.
摘要：
科学被认为是一个累积的过程(“站在巨人的肩膀上”)，然而，科学知识通常是建立在没有太多协调的研究贡献的拼凑上。在临床研究中，针对生活系统评价和研究浪费的建议已特别解决了这种缺乏效率的问题。我们建议通过ALL-IN荟萃分析来进一步提出这些建议：AnytimeLive和LeadingINterim荟萃分析。ALL-IN为荟萃分析提供了统计方法，可以在每次新观察后随时更新-重新分析，同时保留I型错误保证，生活-不需要预先指定的外观，并主导决定是否应该开始个别研究，停止或展开，荟萃分析可能是主要的信息来源。我们说明了时间到事件数据的方法，显示当研究本身缓慢以提供完成所需的事件数量时，在研究的中期阶段合成数据如何提高效率。荟萃分析可以对临时数据进行，但不必。分析设计不需要有关试验中患者数量或最终纳入试验数量的信息。所以它可以为生活的系统评价注入活力，通过更好更简单的统计数据，效率,协作和沟通。
