Dr. Noma is a Professor of Biostatistics at the Institute of Statistical Mathematics in Japan. He is an internationally active methodological researcher whose work spans biostatistics, evidence synthesis, causal inference, and clinical epidemiology.

Dr. Noma has published extensively in leading statistical, epidemiological, and medical journals and has made significant contributions to the development of statistical methodology for meta-analysis and evidence synthesis. His methodological work includes random-effects and network meta-analysis, methods for rare events and prediction, higher-order inference, and approaches to quantifying heterogeneity and uncertainty in evidence synthesis. More recently, he has developed and applied methods for target trial emulation and causal inference using large-scale real-world healthcare data. Several of his methodological developments have been implemented in open-source R software, including tools for target trial emulation and advanced meta-analysis, facilitating their use in applied biomedical research.

Dr. Noma has extensive collaborative research experience in cardiovascular disease, diabetes, kidney disease, dementia, geriatrics, and pharmacoepidemiology. A major focus of his current research is the generation of reliable evidence for older adults and other populations who are poorly represented in randomized clinical trials, through the integration of causal inference, real-world data, and evidence synthesis.

Dr. Noma also plays active international roles in the evidence-synthesis community. He serves as an Associate Editor of Research Synthesis Methods and contributes to international activities within the Cochrane community. Through his methodological research, software development, editorial activities, and interdisciplinary collaborations, he works to bridge the gap between modern statistical theory and evidence needed for clinical and public-health decision making.