arXiv subjects

Saverio Fontana

Publications and source records attributed to Saverio Fontana.

3 recordsLinked to original sources

arXiv2026

Edgington's Combination Method for Two-Study Meta-Analysis: An Empirical Evaluation in 1226 Meta-Analyses

Two-study meta-analyses are common in evidence synthesis but pose major statistical challenges. With only two studies, the between-study variance cannot be reliably estimated, rendering standard random-effects methods unstable. Here, we investigate meta-analyses based on Edgington's p-value combination method as an alternative approach, applying it to 1226 two-study meta-analyses from the German Institute for Quality and Efficiency in Health Care (IQWiG). Like fixed-effect meta-analysis, Edgington's method is calibrated under homogeneity. However, it adapts confidence interval width to observed between-study discrepancy without requiring explicit heterogeneity estimation. In all of the examined meta-analyses, this leads to confidence intervals that contain both study-specific estimates but remain informative. Edgington's method agrees with fixed-effect meta-analysis on statistical significance (at two-sided $\alpha$ = 0.05) in 91% of all meta-analyses, but can give wider intervals when study results are discrepant and narrower intervals when results are highly consistent. Weighted extensions of Edgington's method shift point estimates toward the more precise study while preserving much of this adaptive behavior. We conclude that Edgington's method offers a principled and practically useful complement to existing approaches for two-study meta-analysis, occupying a middle ground between standard fixed-effect and random-effects approaches.

stat.AP↗

arXiv2026

Adjusting for Outcome Reporting Bias in Meta-analysis: A Multiple Imputation Approach

Background: Outcome reporting bias (ORB) occurs when study outcomes are selectively reported based on their results. ORB potentially undermines the credibility and validity of meta-analyses and contributes to research waste by distorting overall treatment effects. ORB can be viewed as a missing data problem in which unreported study outcomes introduce bias. Despite the serious implications ORB poses, it remains an underrecognized issue, with only a few adjustment methods available. Methods: We propose an approach that addresses unreported study outcomes in meta-analyses through multiple imputation for univariate and multivariate meta-analysis. To assess the impact of ORB in meta-analyses, we apply our proposed methodology to real clinical data affected by ORB, and conduct a simulation study to evaluate the method's performance under a range of scenarios. Results: The proposed method provides bias-adjusted estimates under assumed selective non-reporting mechanisms. In the application to clinical data, ORB-adjusted estimates were systematically shifted towards less extreme treatment effects compared with naive analyses, highlighting the potential magnitude of ORB in practice. The simulation study shows that the extent of adjustment depends on the assumed selection mechanism and the degree of heterogeneity, with stronger selection leading to larger adjustment. Conclusions: Imputing unreported study outcomes provides a promising approach to address ORB in meta-analyses. The multivariate approach extends ORB adjustment to jointly model correlated outcomes, allowing borrowing of strength across outcomes. Overall, we propose a practical and flexible approach for evaluating the sensitivity of univariate and multivariate meta-analytic conclusions to ORB.

stat.ME↗

arXiv2026

Balancing Evidentiary Value and Sample Size of Adaptive Designs with Application to Animal Experiments

Reducing the number of experimental units is one of the three pillars of the 3R principles (Replace, Reduce, Refine) in animal research. At the same time, statistical error rates need to be controlled to enable reliable inferences and decisions. This paper proposes to adopt diagnostic likelihood ratios and the diagnostic odds ratio to statistical hypothesis tests and to adjust it for sample size to obtain a novel measure to quantify for the evidentiary value of one experimental unit. The experimental unit information index (EUII) is based on power, Type-I error and sample size, and has attractive interpretations both in terms of frequentist error rates and Bayesian posterior odds. We introduce the EUII in simple statistical test settings and show that its asymptotic value depends only on the assumed relative effect size under the alternative. We then extend the definition to adaptive designs where early stopping for efficacy or futility may cause reductions in sample size. Application to group-sequential designs show the usefulness of the approach when the goal is to maximize the evidentiary value of one experimental unit. A reanalysis of 2738 animal experiments with simulated results from (post-hoc) interim analyses illustrates the possible savings in sample size.

stat.ME↗