CEN 2026 | Central European Network Conference

Central European Network Conference 2026 explores the transformative role of data in life sciences under the theme Power of Data – Shaping the Future of Life Sciences. Join researchers and experts in Warsaw to uncover how biometrics, biostatistics, and data-driven innovation are revolutionizing healthcare, medicine, and environmental science. The conference is held on May 18-21, 2026 in Warsaw

GMDS Biostatistics Competition 2026

PHYSTAT - Statistics meets ML - VERaIPHY

Second PHYSTAT Topical Meeting on “Statistics meets ML” in Particle Physics & Astrophysics – VERaIPHY In recent years, ML has become more and more integrated into many stages of our analyses. In Particle Physics, it includes data collection and processing (triggering, tracking, etc), classification of different particle types, unfolding, parameter determination, anomaly detection, and even end-to-end processing. In astronomy, its use is increasingly widespread in areas such as object...

Consistent Bayesian meta-analysis on subgroup specific effects and interactions

Commonly, clinical trials report effects not only for the full study population but also for patient subgroups. Meta-analyses of subgroup-specific effects and treatment-by-subgroup interactions may be inconsistent, especially when trials apply different subgroup weightings. We show that meta-regression can, in principle, with a contribution adjustment, recover the same interaction inference regardless of whether interaction data or subgroup data are used. Our Bayesian framework for subgroup-data interaction meta-analysis inherently (i) adjusts for varying relative subgroup contribution, quantified by the information fraction (IF) within a trial; (ii) is robust to prevalence imbalance and variation; (iii) provides a self-contained, model-based approach; and (iv) can be used to incorporate prior information into interaction meta-analyses with few studies.The method is demonstrated using an example with as few as seven trials of disease-modifying therapies in relapsing-remitting multiple sclerosis. The Bayesian Contribution-adjusted Meta-analysis by Subgroup (CAMS) indicates a stronger treatment-by-disability interaction (relapse rate reduction) in patients with lower disability (EDSS <= 3.5) compared with the unadjusted model, while results for younger patients (age < 40 years) are unchanged.By controlling subgroup contribution while retaining subgroup interpretability, this approach enables reliable interaction decision-making when published subgroup data are available.Although the proposed CAMS approach is presented in a Bayesian context, it can also be implemented in frequentist or likelihood frameworks.