arXiv subjects

Kotaro Sasaki

Publications and source records attributed to Kotaro Sasaki.

3 recordsLinked to original sources

arXiv2026

Penalized likelihood inference for beta-binomial meta-analysis of proportions of rare events

Meta-analyses of proportions often involve sparse event counts and zero-event studies. The beta-binomial model has been used as a flexible random-effects model for pooling overdispersed and rare-event proportions. However, the ordinary maximum likelihood estimator (MLE) may suffer from finite-sample bias when few studies are available or the mean event probability is close to the boundary. In this article, we propose a maximum penalized likelihood estimator based on the Jeffreys-prior penalty, which has shown favorable finite-sample bias and stability properties in sparse-data models. We also develop Wald-type and profile penalized likelihood confidence intervals (CIs). In a simulation study, the proposed estimator generally achieved higher convergence rates, lower bias, and lower root mean squared error than the ordinary MLE, particularly with fewer studies or lower event probabilities. Additionally, the profile penalized likelihood CIs maintained coverage close to the nominal level. The proposed method provides a useful alternative for meta-analyses of rare-event proportions.

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arXiv2026

Robust inference methods of diagnostic test accuracy meta-analysis for influential outlying studies via density power divergence

In diagnostic test accuracy meta-analysis (DTA-MA), standard inference methods using bivariate random-effects models for jointly synthesizing sensitivity and specificity can be sensitive to outlying studies and may yield misleading conclusions. In this article, we propose frequentist outlier-robust statistical inference methods for DTA-MA based on density power divergence. The proposed methods automatically downweight influential outlying studies by modifying the estimating function using the robust divergence with a tuning parameter. To achieve robust yet statistically efficient inference in the presence of outlying studies, the proposed methods incorporate practical strategies for selecting the tuning parameter, including a data-adaptive criterion based on the Hyvärinen score. We also quantify the contributions of individual studies to the robust pooled estimates, facilitating interpretation of how outlying studies affect the results. We illustrate the effectiveness of the proposed methods through an application to a DTA-MA of the Mini-Mental State Examination. Simulation studies showed that the proposed methods reduced bias and root mean squared error relative to existing methods and improved coverage probability in the presence of outliers. The proposed methods enable a sensitivity analysis to assess whether the main results obtained using standard methods are driven by outlying studies.

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arXiv2025

Influence analyses of "designs" for evaluating inconsistency in network meta-analysis

Network meta-analysis is an evidence synthesis method for comparing the effectiveness of multiple available treatments. To justify evidence synthesis, consistency is an important assumption; however, existing methods founded on statistical testing can be substantially limited in statistical power or have several drawbacks when handling multi-arm studies. Moreover, inconsistency can be theoretically explained as design-by-treatment interactions, and the primary purpose of such analyses is to prioritize the further investigation of specific "designs" to explore sources of bias and other issues that might influence the overall results. In this article, we propose an alternative framework for evaluating inconsistency using influence diagnostics methods, which enable the influence of individual designs on the overall results to be quantitatively evaluated. We provide four new methods, the averaged studentized residual, MDFFITS, Φ_d, and Ξ_d, to quantify the influence of individual designs through a "leave-one-design-out" analysis framework. We also propose a simple summary measure, the O-value, for prioritizing designs and interpreting these influential analyses in a straightforward manner. Furthermore, we propose another testing approach based on the leave-one-design-out analysis framework. By applying the new methods to a network meta-analysis of antihypertensive drugs and performing simulation studies, we demonstrate that the new methods accurately located potential sources of inconsistency. The proposed methods provide new insights into alternatives to existing test-based methods, especially the quantification of the influence of individual designs on the overall network meta-analysis results.

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