Improved methods to construct prediction intervals for network meta‐analysis

Abstract

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. In this article, we performed simulation studies to assess the validity of the current standard method for network meta‐analysis, and we show that its validity can also be violated under realistic situations. To address the invalidity issue, we developed two new methods to construct more accurate prediction intervals through bootstrap and Kenward–Roger‐type adjustment. In simulation experiments, the two proposed methods exhibited better coverage performance and generally provided wider prediction intervals than the ordinary t‐approximation. We also developed an R package, PINMA (https://cran.r-project.org/web/packages/PINMA/), to perform the proposed methods using simple commands. We illustrate the effectiveness of the proposed methods through applications to two real network meta‐analyses.

ネットワークメタ回帰を用いたSidesplitting

“…Results of the sidesplitting analysis for the network meta-analysis of diabetes data † . (Chaimani and Salanti, 2015;Noma et al, 2023b) will also be applicable under this formulation. In network meta-analysis practice, the proposed method would be an effective tool for preventing misleading results and providing precise evidence.…”

Particle Swarm Training of a Neural Network for the Lower Upper Bound Estimation of the Prediction Intervals of Time Series

“…Time series models, the forecast of which is constructed in the form of value boundaries, are called Prediction Intervals (PIs) [6]. The development of robust predictive interval models in information-analytical systems and decision-making systems is a relevant task with significant practical importance in modern big data processing systems [7][8][9][10][11].…”