2013 Fiscal Year Final Research Report
Development of statistical methods utilizing longitudinal data and time to event data effectively.
| Project/Area Number |
22700297
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Statistical science
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| Research Institution | Kurume University |
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Principal Investigator |
YONEMOTO Koji 久留米大学, バイオ統計センター, 講師 (90398090)
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| Project Period (FY) |
2010-04-01 – 2014-03-31
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| Keywords | 同時モデリング / 非線形 / 縮小ランク回帰法 / 疫学 / 生存時間データ / 栄養疫学 / 経時データ |
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| Research Abstract |
For effect of risk factors on development of diseases, we proposed a modeling algorithm which can detect nonlinearity of the effect and confirmed performance of it by a simulation study. We showed that using joint modeling can reduce effect of regression dilution bias, i.e., joint modeling may be useful for observational studies. In nutrition epidemiology, variability of dietary patterns extracted by using reduced rank regression with biomarkers is large so generalizability of the pattern may be problematic. Moreover, we conducted many epidemiological studies, and many papers were published in international journals.
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Research Products
(32 results)
