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- W4207055830 abstract "Cumulative meta-analysis (CMA) is a process of updating the results of existing meta-analysis to incorporate new study results. This is a popular way to present time-varying evidence. We investigate the properties of CMA, suggest possible improvements and provide the first in-depth simulation study of the use of CMA and CUSUM methods for detection of temporal trends in random-effects meta-analysis. We use the standardized mean difference (SMD) as an effect measure of interest. For CMA, we compare the standard inverse-variance-weighted estimation of the overall effect using REML-estimated between-study variance $tau^2$ with the sample-size-weighted estimation of the effect combined with Kulinskaya-Dollinger-Bjørkestøl (2011) (KDB) estimation of $tau^2$. For all methods, we consider type 1 error under no shift and power under shift in the mean. To ameliorate the lack of power in CMA, we introduce the two-stage CMA, where the heterogeneity variance $tau^2$ is estimated at stage 1 (first 5-10 studies), and the further CMA monitors a target value of effect, keeping the $tau^2$ value fixed. We recommend the use of this two-stage CMA combined with cumulative testing for positive shift in $tau^2$." @default.
- W4207055830 created "2022-01-26" @default.
- W4207055830 creator A5001621375 @default.
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- W4207055830 date "2021-01-05" @default.
- W4207055830 modified "2023-09-28" @default.
- W4207055830 title "Simulation results on the performance of statistical methods in cumulative meta analysis" @default.
- W4207055830 doi "https://doi.org/10.31222/osf.io/8t4pf" @default.
- W4207055830 hasPublicationYear "2021" @default.
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