Refined trial sequential analysis for meta-analyses with few studies

Evidence syntheses are often updated as new trials become available. A cumulative meta-analysis repeats a meta-analysis in chronological order, and trial sequential analysis applies group-sequential principles to cumulative meta-analysis to control the risk of spurious findings due to repeated testing. Despite the growing popularity of trial sequential analysis, existing methods rely heavily on normal approximations derived from interim analyses of randomized controlled trials, where participants are typically more homogeneous than in meta-analyses. In random-effects meta-analyses, the conventional assumption that the synthesized effect estimate follows a normal distribution can perform poorly when the number of studies is small. In such settings, the Hartung-Knapp-Sidik-Jonkman method, which is based on a $t$ distribution, has been recommended for more reliable inference. This article introduces refined trial sequential procedures based on cumulative meta-analytic $t$ statistics. The proposed methods are designed to reduce the risk of premature or spurious conclusions in updating evidence syntheses, particularly when between-study heterogeneity is present. Numerical studies demonstrate that the proposed methods provide improved control of type I error compared with existing methods, although the degree of improvement depends on the magnitude of heterogeneity and the true effect size.