Journal Article

Trial Sequential Analysis in systematic reviews with meta-analysis

Wetterslev, Jørn, Gluud, Christian, Jakobsen, Janus Christian

2017

Overview

Background Most meta-analyses in systematic reviews, including Cochrane ones, do not have sufficient statistical power to detect or refute even large intervention effects. This is why a meta-analysis ought to be regarded as an interim analysis on its way towards a required information size. The results of the meta-analyses should relate the total number of randomised participants to the estimated required meta-analytic information size accounting for statistical diversity. When the number of participants and the corresponding number of trials in a meta-analysis are insufficient, the use of the traditional 95% confidence interval or the 5% statistical significance threshold will lead to too many false positive conclusions (type I errors) and too many false negative conclusions (type II errors). Methods We developed a methodology for interpreting meta-analysis results, using generally accepted, valid evidence on how to adjust thresholds for significance in randomised clinical trials when the required sample size has not been reached. Results The Lan-DeMets trial sequential monitoring boundaries in Trial Sequential Analysis offer adjusted confidence intervals and restricted thresholds for statistical significance when the diversity-adjusted required information size and the corresponding number of required trials for the meta-analysis have not been reached. Trial Sequential Analysis provides a frequentistic approach to control both type I and type II errors. We define the required information size and the corresponding number of required trials in a meta-analysis and the diversity (D 2 ) measure of heterogeneity. We explain the reasons for using Trial Sequential Analysis of meta-analysis when the actual information size fails to reach the required information size. We present examples drawn from traditional meta-analyses using unadjusted naïve 95% confidence intervals and 5% thresholds for statistical significance. Spurious conclusions in systematic reviews with traditional meta-analyses can be reduced using Trial Sequential Analysis. Several empirical studies have demonstrated that the Trial Sequential Analysis provides better control of type I errors and of type II errors than the traditional naïve meta-analysis. Conclusions Trial Sequential Analysis represents analysis of meta-analytic data, with transparent assumptions, and better control of type I and type II errors than the traditional meta-analysis using naïve unadjusted confidence intervals.

• Publisher: BioMed Central,Springer Nature B.V,BMC
• Subject: Clinical trials / Confidence intervals / Data analysis / Evidence-based medicine / Fixed-effect model / Group sequential analysis / Health Sciences / Humans / Hypotheses / Interim analysis / Intervention / Medicine / Medicine & Public Health / Meta-analysis / Meta-Analysis as Topic / Mortality / Random-effects model / Randomized Controlled Trials as Topic / Research Article / Research Design / Sample Size / Statistical Theory and Methods / statistics and modelling / Statistics for Life Sciences / Surgery / Theory of Medicine/Bioethics / Trial sequential analysis