Publications by authors named "Yasuyuki Hamura"
Article Synopsis
- Network meta-analysis is crucial for comparing treatment effectiveness, but standard methods like REML often underestimate statistical errors in confidence intervals.
- Recent improvements using higher-order asymptotic approximations offer better accuracy for these analyses, particularly with covariance matrix estimators.
- Simulation studies showed that new Kenward-Roger-type methods provide more reliable coverage compared to traditional REML-based confidence intervals, as demonstrated in real dataset applications.
Improved methods to construct prediction intervals for network meta-analysis.
Res Synth Methods
November 2023
Network meta-analysis has played an important role in evidence-based medicine for assessing the comparative effectiveness of multiple available treatments. The prediction interval has been one of the standard outputs in recent network meta-analysis as an effective measure that enables simultaneous assessment of uncertainties in treatment effects and heterogeneity among studies. To construct the prediction interval, a large-sample approximating method based on the t-distribution has generally been applied in practice; however, recent studies have shown that similar t-approximation methods for conventional pairwise meta-analyses can substantially underestimate the uncertainty under realistic situations.
View Article and Find Full Text PDF