時間依存共変量を伴う Cox回帰モデルにおける欠測の問題とその対処法
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| Published in | 計量生物学 Vol. 45; no. 2; pp. 287 - 308 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | Japanese |
| Published |
日本計量生物学会
30.11.2024
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| Online Access | Get full text |
| ISSN | 0918-4430 2185-6494 |
| DOI | 10.5691/jjb.45.287 |
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| Author | 杉本, 知之 田中, 健太 |
|---|---|
| Author_xml | – sequence: 1 fullname: 杉本, 知之 organization: 大阪大学大学院基礎工学研究科 – sequence: 1 fullname: 田中, 健太 organization: 国立循環器病研究センター |
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| Copyright | 2024 日本計量生物学会 |
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| DOI | 10.5691/jjb.45.287 |
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| References | Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons. Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. CRC press. Barnard, J. and Rubin, D. B. (1999). Small-sample degrees of freedom with multiple imputation. Biometrika, 86, 948-955. Carroll, O. U., Morris, T. P. and Keogh, R. H. (2020). How are missing data in covariates handled in observational time-to-event studies in oncology? A systematic review. BMC Medical Research Methodology, 20, 134. Moreno-Betancur, M., Carlin, J. B., Brilleman, S. L., Tanamas, S. K., Peeters, A. and Wolfe, R. (2018). Survival analysis with time-dependent covariates subject to missing data or measurement error: Multiple Imputation for Joint Modeling (MIJM). Biostatistics, 19, 479-496. Mantel, N. (1966). Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemotherapy Reports, 50, 163-170. Rasmussen, C. E. and Williams, C. K. (2006). Gaussian Processes for Machine Learning. MIT Press. van Buuren, S. and Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45, 1-67. Andersen, P. K. and Gill, R. D. (1982). Cox's regression model for counting processes: a large sample study. The Annals of Statistics, 10, 1100-1120. Takeuchi, Y., Ogawa, M., Hagiwara, Y. and Matsuyama, Y. (2021). Non-parametric approach for frequentist multiple imputation in survival analysis with missing covariates. Statistical Methods in Medical Research, 30, 1691-1707. White, I. R. and Royston, P. (2009). Imputing missing covariate values for the Cox model. Statistics in Medicine, 28, 1982-1998. 高井啓二，星野崇宏，野間久史(2016). 欠測データの統計科学─医学と社会科学への応用（調査観察データ解析の実際 1）． 岩波書店 Kaplan, E. L. and Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53, 457-481. 野間久史，田中司朗，田中佐智子，和泉志津恵(2013). Multiple Imputation法によるネステッドケースコントロール研究， ケースコホート研究の解析． 計量生物学， 33, 101-124. Royston, P. and White, I. R. (2011). Multiple imputation by chained equations (MICE): Implementation in Stata. Journal of Statistical Software, 45, 1-20. Wu, L., Liu, W., Yi, G. Y. and Huang, Y. (2012). Analysis of longitudinal and survival data: joint modeling, inference methods, and issues. Journal of Probability and Statistics, 2012, 1-17. Bartlett, J. W., Seaman, S. R., White, I. R., Carpenter, J. R. and Alzheimer's Disease Neuroimaging Initiative*. (2015). Multiple imputation of covariates by fully conditional specification: accommodating the substantive model. Statistical Methods in Medical Research, 24, 462-487. Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B, 34, 187-202. Sterne, J. A., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M. and Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ (Clinical Research Ed.), 338, b2393. 持橋大地，大羽成征(2019). ガウス過程と機械学習（機械学習プロフェッショナルシリーズ）． 講談社 Moons, K. G., Donders, R. A., Stijnen, T. and Harrell Jr, F. E. (2006). Using the outcome for imputation of missing predictor values was preferred. Journal of Clinical Epidemiology, 59, 1092-1101. White, I. R., Royston, P. and Wood, A. M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30, 377-399. |
| References_xml | – reference: White, I. R., Royston, P. and Wood, A. M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30, 377-399. – reference: 野間久史，田中司朗，田中佐智子，和泉志津恵(2013). Multiple Imputation法によるネステッドケースコントロール研究， ケースコホート研究の解析． 計量生物学， 33, 101-124. – reference: Moreno-Betancur, M., Carlin, J. B., Brilleman, S. L., Tanamas, S. K., Peeters, A. and Wolfe, R. (2018). Survival analysis with time-dependent covariates subject to missing data or measurement error: Multiple Imputation for Joint Modeling (MIJM). Biostatistics, 19, 479-496. – reference: Kaplan, E. L. and Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53, 457-481. – reference: 高井啓二，星野崇宏，野間久史(2016). 欠測データの統計科学─医学と社会科学への応用（調査観察データ解析の実際 1）． 岩波書店． – reference: Royston, P. and White, I. R. (2011). Multiple imputation by chained equations (MICE): Implementation in Stata. Journal of Statistical Software, 45, 1-20. – reference: Rasmussen, C. E. and Williams, C. K. (2006). Gaussian Processes for Machine Learning. MIT Press. – reference: Sterne, J. A., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M. and Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ (Clinical Research Ed.), 338, b2393. – reference: White, I. R. and Royston, P. (2009). Imputing missing covariate values for the Cox model. Statistics in Medicine, 28, 1982-1998. – reference: Carroll, O. U., Morris, T. P. and Keogh, R. H. (2020). How are missing data in covariates handled in observational time-to-event studies in oncology? A systematic review. BMC Medical Research Methodology, 20, 134. – reference: Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons. – reference: Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B, 34, 187-202. – reference: van Buuren, S. and Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45, 1-67. – reference: Takeuchi, Y., Ogawa, M., Hagiwara, Y. and Matsuyama, Y. (2021). Non-parametric approach for frequentist multiple imputation in survival analysis with missing covariates. Statistical Methods in Medical Research, 30, 1691-1707. – reference: Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. CRC press. – reference: 持橋大地，大羽成征(2019). ガウス過程と機械学習（機械学習プロフェッショナルシリーズ）． 講談社． – reference: Mantel, N. (1966). Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemotherapy Reports, 50, 163-170. – reference: Moons, K. G., Donders, R. A., Stijnen, T. and Harrell Jr, F. E. (2006). Using the outcome for imputation of missing predictor values was preferred. Journal of Clinical Epidemiology, 59, 1092-1101. – reference: Andersen, P. K. and Gill, R. D. (1982). Cox's regression model for counting processes: a large sample study. The Annals of Statistics, 10, 1100-1120. – reference: Barnard, J. and Rubin, D. B. (1999). Small-sample degrees of freedom with multiple imputation. Biometrika, 86, 948-955. – reference: Bartlett, J. W., Seaman, S. R., White, I. R., Carpenter, J. R. and Alzheimer's Disease Neuroimaging Initiative*. (2015). Multiple imputation of covariates by fully conditional specification: accommodating the substantive model. Statistical Methods in Medical Research, 24, 462-487. – reference: Wu, L., Liu, W., Yi, G. Y. and Huang, Y. (2012). Analysis of longitudinal and survival data: joint modeling, inference methods, and issues. Journal of Probability and Statistics, 2012, 1-17. |
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| Title | 時間依存共変量を伴う Cox回帰モデルにおける欠測の問題とその対処法 |
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