Department of Biostatistics, Julius Center, University Medical Center, Utrecht, The Netherlands.
Clin Trials. 2010 Apr;7(2):136-46. doi: 10.1177/1740774509360994. Epub 2010 Mar 25.
A cumulative meta-analysis of successive randomized controlled trials (RCTs) can be used to decide whether enough evidence has been obtained comparing a control and an intervention treatment or whether a new RCT should be initiated. In general, no adjustment is made for repeatedly testing the null hypothesis of treatment equivalence on cumulative data. Neither can the power of the statistical test be quantified. Recently, trial sequential analysis (TSA) was suggested to '. . . establish when firm evidence is reached in cumulative meta-analysis'. TSA is based on alpha-spending functions and necessitates a prior estimate of the total information size. Various information sizes were suggested.
The aim of this study is to compare TSA with sequential meta-analysis (SMA) following Whitehead's boundaries approach.
We compare TSA and SMA by re-analysis of a number of published examples.
Re-analysis of the examples shows that for an SMA: (1) no prior estimate for total information size is necessary and thus one set of boundaries suffices; (2) stopping a cumulative meta-analysis for futility is an option; (3) the power can be quantified; (4) point and interval estimates are adjusted for the multiple testing; and (5) gains in efficiency can be achieved, both for efficacy and for futility and thus ethical and economical benefits can be obtained.
Estimates for between-trial variability are unstable for a small number of trials. The behavior of a newly proposed estimate should be subject of further investigation.
SMA is a useful tool to investigate the cumulative evidence from successive RCTs.
连续随机对照试验（RCT）的累积荟萃分析可用于确定是否已经获得足够的证据来比较对照和干预治疗，或者是否应该启动新的 RCT。一般来说，对于累积数据，不会对治疗等效性的零假设进行多次检验调整。也无法量化统计检验的功效。最近，试验序贯分析（TSA）被建议用于“在累积荟萃分析中何时达到确凿证据”。TSA 基于 alpha 支出函数，并需要事先估计总信息量。已提出了各种信息量。
本研究旨在比较 TSA 与 Whitehead 边界法下的序贯荟萃分析（SMA）。
我们通过重新分析一些已发表的例子来比较 TSA 和 SMA。
对这些例子的重新分析表明，对于 SMA：（1）不需要对总信息量进行事先估计，因此只需一组边界；（2）可以选择停止累积荟萃分析的无效性；（3）可以量化功效；（4）点估计和区间估计会针对多次检验进行调整；（5）可以提高效率，无论是对疗效还是对无效性，从而获得伦理和经济利益。
对于少数试验，试验间变异性的估计不稳定。新提出的估计的行为应进一步调查。
SMA 是一种有用的工具，可以用于研究连续 RCT 的累积证据。
