Semiparametric inferences for time-to-event data with incomplete data and their multidimensional extensions
| Project/Area Number |
17K00054
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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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| Research Field |
Statistical science
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| Research Institution | Shiga University (2019-2020)
Kagoshima University (2017-2018) |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 生存分析 / 事象時間データ / 回帰分析 / 多変量分布 / 数理統計 / 多変量解析 / 層別分析 / 競合リスク問題 / 群逐次デザイン / セミパラメトリック解析 / ノンパラメトリック解析 / 繰り返し測定解析 / 樹木構造接近法 / マルチンゲール接近法 / コピュラモデル / ログランク統計量 / ノンパラメトリック法 / 統計推測 / データサイエンス / 統計数学 / 臨床統計 |
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| Outline of Final Research Achievements |
We formulated the asymptotic distribution of correlated bivariate log-rank statistics and applied it to inference for bivariate event-time data with copula-type correlation. Some results were obtained in the studies of inference for the semi-competitive risk problem, and application of group-sequential bivariate log-rank statistics to sample size design. We also studied a semi-parametric estimation method using survival regression trees for relative survival, and proposed a Brier score for relative survival models to measure the predictive performance of regression trees, which was presented at a conference. We studied semi-parametric estimation of bivariate hazard models in which event-time and calendar-time are represented separately, and examined computational methods for the semi-parametric estimation.
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| Academic Significance and Societal Importance of the Research Achievements |
医療の世界では，がんや循環器疾患などで治療効果を測るために，事象時間データの分析は必須である．経済分野では倒産などのイベントを分析したり，製造業分野では在庫がなくなるまでの時間を分析したり，事象時間データの分析の適用例は多くみられる．そのようなデータの分析方法について，Cox回帰モデルなどの生存時間データの統計解析法は必須であり，その当該分野において，現在まで得られている統計的な分析方法，理論，計算手法を発展させるための研究を行い，一定の成果を得ることができたことは，学術的意義をもつ．さらに，これらの手法を実際のデータに応用していくことで，社会的に還元をなすことができる．
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Report
(5 results)
Research Products
(18 results)
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[Journal Article] Predicted Prognosis of Pancreatic Cancer Patients by Machine Learning.2020
Author(s)
Seiya Yokoyama, Taiji Hamada, Michiyo Higashi, Kei Matsuo, Kousei Maemura, Hiroshi Kurahara, Michiko Horinouchi, Tsubasa Hiraki, Tomoyuki Sugimoto, Toshiaki Akahane, Suguru Yonezawa, Marko Kornmann, Surinder K Batra, Michael A Hollingsworth, Akihide Tanimoto
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Journal Title
Clinical cancer research
Volume: -
Issue: 10
Pages: 2411-2421
DOI
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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