Sequential meta-analysis: an efficient decision-making tool

Abstract

BACKGROUND: 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. PURPOSE: The aim of this study is to compare TSA with sequential meta-analysis (SMA) following Whitehead's boundaries approach. METHODS: We compare TSA and SMA by re-analysis of a number of published examples. RESULTS: 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. LIMITATIONS: 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. CONCLUSION: SMA is a useful tool to investigate the cumulative evidence from successive RCTs.