165 citations · 432 across the 12 of their papers we have counts for
12 papers
Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies
David Benrimoh, Akiva Kleinerman, Toshi A. Furukawa +17
Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological substrates could be associated with treatment response variability. Understanding…
Synthesizing cross-design evidence and cross-format data using network meta-regression
Tasnim Hamza, Konstantina Chalkou, Fabio Pellegrini +9
In network meta-analysis (NMA), we synthesize all relevant evidence about health outcomes with competing treatments. The evidence may come from randomized controlled trials (RCT) o…
pimeta: an R package of prediction intervals for random-effects meta-analysis
Kengo Nagashima, Hisashi Noma, Toshi A. Furukawa
The prediction interval is gaining prominence in meta-analysis as it enables the assessment of uncertainties in treatment effects and heterogeneity between studies. However, covera…
A dose-effect network meta-analysis model: an application in antidepressants
Tasnim Hamza, Toshi A. Furukawa, Nicola Orsini +3
Network meta-analysis (NMA) has been used to answer a range of clinical questions about the preferable intervention for a given condition. Although the effectiveness and safety of…
Confidence intervals of prediction accuracy measures for multivariable prediction models based on the bootstrap-based optimism correction methods
Hisashi Noma, Tomohiro Shinozaki, Katsuhiro Iba +2
In assessing prediction accuracy of multivariable prediction models, optimism corrections are essential for preventing biased results. However, in most published papers of clinical…
A Bayesian dose-response meta-analysis model: simulation study and application
Tasnim Hamza, Andrea Cipriani, Toshi A. Furukawa +3
Dose-response models express the effect of different dose or exposure levels on a specific outcome. In meta-analysis, where aggregated-level data is available, dose-response eviden…
Flexible random-effects distribution models for meta-analysis
Hisashi Noma, Kengo Nagashima, Shogo Kato +2
In meta-analysis, the random-effects models are standard tools to address between-study heterogeneity in evidence synthesis analyses. For the random-effects distribution models, th…
Outlier detection and influence diagnostics in network meta-analysis
Hisashi Noma, Masahiko Gosho, Ryota Ishii +2
Network meta-analysis has been gaining prominence as an evidence synthesis method that enables the comprehensive synthesis and simultaneous comparison of multiple treatments. In ma…
Frequentist performances of Bayesian prediction intervals for random-effects meta-analysis
Yuta Hamaguchi, Hisashi Noma, Kengo Nagashima +2
The prediction interval has been increasingly used in meta-analyses as a useful measure for assessing the magnitude of treatment effect and between-studies heterogeneity. In calcul…
Bayesian influence diagnostics and outlier detection for meta-analysis of diagnostic test accuracy
Yuki Matsushima, Hisashi Noma, Tomohide Yamada +1
Meta-analyses of diagnostic test accuracy (DTA) studies have been gaining prominence in research in clinical epidemiology and health technology development. In these DTA meta-analy…
Permutation inference methods for multivariate meta-analysis
Hisashi Noma, Kengo Nagashima, Toshi A. Furukawa
Multivariate meta-analysis is gaining prominence in evidence synthesis research because it enables simultaneous synthesis of multiple correlated outcome data, and random-effects mo…
Prediction intervals for random-effects meta-analysis: a confidence distribution approach
Kengo Nagashima, Hisashi Noma, Toshi A. Furukawa
Prediction intervals are commonly used in meta-analysis with random-effects models. One widely used method, the Higgins-Thompson-Spiegelhalter prediction interval, replaces the het…