2023 Fiscal Year Final Research Report
Development of statistical methods for low-dose radiation risk assessment
| Project/Area Number |
20K11721
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 60030:Statistical science-related
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| Research Institution | Kurume University |
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Principal Investigator |
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | リスク解析 / 生存時間分析 / Frailty model / 放射線疫学 / 発がんモデル |
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| Outline of Final Research Achievements |
With increasing opportunities for radiation exposure from medical, occupational, and environmental sources, appropriate evaluation of radiation-associated risks is required. This study developed a modeling framework suitable for correct understanding of the radiation risks in epidemiological studies. First, we formulated the Poisson survival time regression method as a flexible risk analysis method that enables us to evaluate the dose response and the time-dependent pattern of risk in more detail and evaluated its estimation performance. To address potential problems such as heterogeneity due to unobserved factors such as individual differences in radiosensitivity and correlations among multiple survival endpoints including competing risks, we developed a risk analysis method extended to a generalized linear mixed effects model including random effects (frailty).
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| Free Research Field |
生物統計学、疫学
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は、放射線リスク評価において、これまで十分に検討されなかった不確実性を持つ要因、特にリスクの経時的変化と放射線感受性の個人差に焦点を当て、低線量被曝に伴うリスクの評価や予測に適した新しいモデリングと統計手法の開発を行った。本研究の成果は、低線量放射線リスクの評価結果に新たな知見を与え、安全で適切な放射線利用のための根拠に基づく意思決定のための有益な情報として、放射線防護への貢献が期待される。
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