AI 中文总结
ALL-IN meta分析是随时有效的方法，可用于前瞻性或回顾性证据合成，能提升灵活性与有效性，但在分析师可控制最大样本量或停止规则时会效率降低。
AI 中文摘要
ALL-IN meta分析是在新冠疫情期间开发并首次应用的方法，尽管其名称源于该疫情场景，但也可惠及非疫情情境。传统meta分析在随时间反复更新时，以及启动新试验并将其纳入合成的决策依赖于meta分析内部结果时，会丧失覆盖性（即累积偏差）。ALL-IN meta分析是随时有效的；其最简单形式的运行和解读仍基于森林图，只是置信区间比标准的更宽。在协作型前瞻性meta分析中，其优势是灵活性与速度，可快速共享个体参与者数据或经协调的汇总数据；在该场景之外，当塑造证据的决策（何时停止试验、是否启动新试验）通常不受meta分析师控制时，其优势是有效性。一方面，当存在meta分析师可控制的最大样本量或停止规则时，ALL-IN meta分析会变得低效；另一方面，它能为任何证据合成提供适配，使其在中途成为基于试验中期结果的动态、前瞻性甚至实时的分析，且不会使统计方法复杂化。
英文摘要
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.
CommentsCommentary, invited contribution to the special issue of the journal Cochrane Evidence Synthesis and Methods, guest edited by Prof. Anna Lene Seidler and Dr. Peter Godolphin Collaborative Evidence Synthesis: Individual Participant Data, Prospective Meta-analysis, and Other Approaches
