経時測定バイオマーカーを用いた生存時間アウトカムに対する動的予測 ジョイントモデルとランドマークアプローチ
Saved in:
| Published in | 計量生物学 Vol. 45; no. 2; pp. 189 - 214 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
日本計量生物学会
30.11.2024
|
| Online Access | Get full text |
| ISSN | 0918-4430 2185-6494 |
| DOI | 10.5691/jjb.45.189 |
Cover
| Author | 横田, 勲 坂巻, 顕太郎 大庭, 幸治 アルアリアシー, らるび |
|---|---|
| Author_xml | – sequence: 1 fullname: 坂巻, 顕太郎 organization: 順天堂大学 健康データサイエンス学部 – sequence: 1 fullname: 横田, 勲 organization: 北海道大学 大学院医学研究院 医学統計学教室 – sequence: 1 fullname: アルアリアシー, らるび organization: 国際医療福祉大学 研究支援センター – sequence: 1 fullname: 大庭, 幸治 organization: 東京大学 大学院医学系研究科 公共健康医学専攻 生物統計学分野 |
| BookMark | eNo9kEtLw0AAhBepYK29-D9Sd7O7ye7BgxRfUPCi55jHRhtqlaQXj9n0UKgH0Sq2BysiItbHSRAR_8yaBv-FsYpzmIGPYQ4zCwrN_aYAYB7BCjU4WggCp0JoBTE-BYo6YlQzCCcFUIQcMY0QDGdAOYoCmMuAGHOzCLazl6NxX45fH9KngUqOlbxR8l4llyp5V3I08ZOsd6fitoqHWW-YPl7k_a_zUyWvlbxVSWdSu1LxKH3-UHFfyW7aPcsG7c-3Tj47B6Z9uxGJ8l-WwNbK8mZ1TattrK5Xl2pagDBjmun6UOiIEsiw8E3PYT5FVAhBPJ-ZnvB006MCEp8KjjzDyAlC1OXYwaYQJsUlsPi7G0Qte0dYB2F9zw4PLTts1d2GsPJzLEIt_cfyh_65u2uHVmDjb2ckgm4 |
| ContentType | Journal Article |
| Copyright | 2024 日本計量生物学会 |
| Copyright_xml | – notice: 2024 日本計量生物学会 |
| DOI | 10.5691/jjb.45.189 |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2185-6494 |
| EndPage | 214 |
| ExternalDocumentID | article_jjb_45_2_45_189_article_char_ja |
| GroupedDBID | ABJNI ACGFS ALMA_UNASSIGNED_HOLDINGS JSF KQ8 OK1 RJT |
| ID | FETCH-LOGICAL-j1388-7cf0e2154083ef7db8f515eee4df87ded27d5e04f5e91d66ded115c93b37ee753 |
| ISSN | 0918-4430 |
| IngestDate | Wed Sep 03 06:30:46 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | Japanese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-j1388-7cf0e2154083ef7db8f515eee4df87ded27d5e04f5e91d66ded115c93b37ee753 |
| OpenAccessLink | https://www.jstage.jst.go.jp/article/jjb/45/2/45_189/_article/-char/ja |
| PageCount | 26 |
| ParticipantIDs | jstage_primary_article_jjb_45_2_45_189_article_char_ja |
| PublicationCentury | 2000 |
| PublicationDate | 2024/11/30 |
| PublicationDateYYYYMMDD | 2024-11-30 |
| PublicationDate_xml | – month: 11 year: 2024 text: 2024/11/30 day: 30 |
| PublicationDecade | 2020 |
| PublicationTitle | 計量生物学 |
| PublicationTitleAlternate | 計量生物学 |
| PublicationYear | 2024 |
| Publisher | 日本計量生物学会 |
| Publisher_xml | – name: 日本計量生物学会 |
| References | Li, Z., Chinchilli, V.M. and Wang, M. (2019). A Bayesian joint model of recurrent events and a terminal event. Biometrical Journal, 61, 187-202. Andrinopoulou, E.-R., Rizopoulos, D., Takkenberg, J.J.M. and Lesaffre, E. (2014). Joint modeling of two longitudinal outcomes and competing risk data. Statistics in Medicine, 33, 3167-3178. Skuladottir, H. and Olsen, J.H. (2003). Conditional survival of patients with the four major histologic subgroups of lung cancer in Denmark. Journal of Clinical Oncology, 21, 3035-3040. Andrinopoulou, E.-R., Rizopoulos, D., Takkenberg, J.J.M. and Lesaffre, E. (2017). Combined dynamic predictions using joint models of two longitudinal outcomes and competing risk data. Statistical Methods in Medical Research, 26, 1787-1801. Zhu, Y., Huang, X. and Li, L. (2020). Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers. Biometrical Journal, 62, 1371-1393. van Houwelingen, H. and Putter, H. (2011). Dynamic Prediction in Clinical Survival Analysis. Chapman and Hall/CRC, Boca Raton, FL. Harhay, M.O., Gasparini, A., Walkey, A.J., Weissman, G.E., Crowther, M.J., Ratcliffe, S.J. et al. (2020). Assessing the Course of Organ Dysfunction Using Joint Longitudinal and Time-to-Event Modeling in the Vasopressin and Septic Shock Trial. Critical Care Explorations, 2, e0104. 江村剛志，道前洋史 (2020). コピュラを用いた生存時間解析： 相関のあるエンドポイントとメタ分析の活用．統計数理， 68, 147-174. Wu, C., Li, L. and Li, R. (2020). Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers. Statistical Methods in Medical Research, 29, 3179-3191. Murray, J. and Philipson, P. (2022). A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data. Computational Statistics and Data Analysis, 170, 107438. Kheirandish, M., Catanzaro, D., Crudu, V. and Zhang, S. (2022). Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes. Journal of the American Medical Informatics Association, 29, 900-908. Sweeting, M.J., Barrett, J.K., Thompson, S.G. and Wood, A.M. (2017). The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study. Statistics in Medicine, 36, 4514-4528. Yokota, I. and Matsuyama, Y. (2019). Dynamic prediction of repeated events data based on landmarking model: application to colorectal liver metastases data. BMC Medical Research Methodology, 19, 31. Spolverato, G., Capelli, G., Lorenzoni, G., Gregori, D., He, J., Popescu, I. et al. (2022b) Dynamic Prediction of Survival After Curative Resection of Intrahepatic Cholangiocarcinoma: A Landmarking-Based Analysis. Annals of Surgical Oncology, 29, 7634-7641. Spolverato, G., Azzolina, D., Paro, A., Lorenzoni, G., Gregori, D., Poultsides, G. et al. (2022a) Dynamic prediction of survival after curative resection of gastric adenocarcinoma: A landmarking-based analysis. European Journal of Surgical Oncology, 48, 1025-1032. Cekic, S., Aichele, S., Brandmaier, A.M., Köhncke, Y. and Ghisletta, P. (2021). A Tutorial for Joint Modeling of Longitudinal and Time-to-Event Data in R. Quantitative and Computational Methods in Behavioral Sciences, 1-40. Murray, J. and Philipson, P. (2023). Fast estimation for generalised multivariate joint models using an approximate EM algorithm. Computational Statistics and Data Analysis, 187, 107819. Yu, M., Taylor, J.M.G. and Sandler, H.M. (2008). Individual prediction in prostate cancer studies using a joint longitudinal survival-cure model. Journal of the American Statistical Association, 103, 178-187. Anderson, J.R., Cain, K.C. and Gelber, R.D. (1983). Analysis of survival by tumor response. Journal of Clinical Oncology, 1, 710-719. van Houwelingen, H.C. (2007). Dynamic prediction by landmarking in event history analysis. Scandinavian Journal of Statistics, 34, 70-85. Huang, X., Li, G., Elashoff, R.M. and Pan, J. (2011). A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects. Lifetime Data Analysis, 17, 80-100. Liang, M., Li, Z., Li, L., Chinchilli, V.M., Zhang, L. and Wang, M. (2023). Tackling dynamic prediction of death in patients with recurrent cardiovascular events. Statistics in Medicine, 42, 3487-3507. Devaux, A., Genuer, R., Peres, K. and Proust-Lima, C. (2022). Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach. BMC Medical Research Methodology, 22, 188. Harshman, L.C., Xie, W., Bjarnason, G.A., Knox, J.J., MacKenzie, M., Wood, L. et al. (2012). Conditional survival of patients with metastatic renal-cell carcinoma treated with VEGF-targeted therapy: a population-based study. The Lancet Oncology, 13, 927-935. Park, K.Y. and Qiu, P. (2014). Model selection and diagnostics for joint modeling of survival and longitudinal data with crossing hazard rate functions. Statistics in medicine, 33, 4532-4546. Staplin, N.D., Kimber, A.C., Collett, D. and Roderick, P.J. (2015). Dependent censoring in piecewise exponential survival models. Statistical methods in medical research, 24, 325-341. Rizopoulos, D. and Ghosh, P. (2011). A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event. Statistics in Medicine, 30, 1366-1380. Hickey, G.L., Philipson, P., Jorgensen, A. and Kolamunnage-Dona, R. (2018). joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes. BMC Medical Research Methodology, 18, 50. Andersen, P.K. and Liestøl, K. (2003). Attenuation caused by infrequently updated covariates in survival analysis. Biostatistics, 4, 633-649. Fournier, M.-C., Foucher, Y., Blanche, P., Legendre, C., Girerd, S., Ladrière, M. et al. (2019). Dynamic predictions of long-term kidney graft failure: an information tool promoting patient-centred care. Nephrology Dialysis Transplantation, 34, 1961-1969. Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. Chapman & Hall/CRC, Boca Raton, FL. Lenain, R., Dantan, E., Giral, M., Foucher, Y., Asar, Ö., Naesens, M. et al. (2021). External validation of the DynPG for kidney transplant recipients. Transplantation, 105, 396-403. Wulfsohn, M.S. and Tsiatis, A.A. (1997). A joint model for survival and longitudinal data measured with error. Biometrics, 53, 330-339. Tsiatis, A.A., Degruttola, V. and Wulfsohn, M.S. (1995). Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association, 90, 27-37. Steyerberg, E.W., Moons, K.G.M., van der Windt, D.A., Hayden, J.A., Perel, P., Schroter, S. et al. (2013). Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Medicine, 10, e1001381. Putter, H. and van Houwelingen, H.C. (2022). Landmarking 2.0: Bridging the gap between joint models and landmarking. Statistics in Medicine, 41, 1901-1917. Rizopoulos, D., Papageorgiou, G. and Miranda Afonso, P. (2023). JMbayes2: Extended Joint Models for Longitudinal and Time-to-Event Data. URL https://drizopoulos.github.io/JMbayes2/index.html [accessed 10 December 2023] Nicolaie, M.A., van Houwelingen, J.C., de Witte, T.M. and Putter, H. (2013). Dynamic prediction by landmarking in competing risks. Statistics in Medicine, 32, 2031-2047. Rizopoulos, D. (2011). Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics, 67, 819-829. Xie, Y., He, Z., Tu, W. and Yu, Z. (2020). Variable selection for joint models with time-varying coefficients. Statistical Methods in Medical Research, 29, 309-322. Heagerty, P.J. and Zheng, Y. (2005). Survival model predictive accuracy and ROC curves. Biometrics, 61, 92-105. Paige, E., Barrett, J., Stevens, D., Keogh, R.H., Sweeting, M.J., Nazareth, I. et al. (2018). Landmark models for optimizing the use of repeated measurements of risk factors in electronic health records to predict future disease risk. American Journal of Epidemiology, 187, 1530-1538. Caillebotte, A., Kuhn, E. and Lemler, S. (2023). Estimation and variable selection in a joint model of survival times and longitudinal outcomes with random effects. arXiv:2306.16765. Lin, J. and Luo, S. (2022). Deep learning for the dynamic prediction of multivariate longitudinal and survival data. Statistics in Medicine, 41, 2894-2907. Murtaugh, P.A., Dickson, E.R., Van Dam, G.M., Malinchoc, M., Grambsch, P.M., Langworthy, A.L. et al. (1994). Primary biliary cirrhosis: prediction of short-term survival based on repeated patient visits. Hepatology, 20, 126-134. Thomadakis, C., Meligkotsidou, L., Yiannoutsos, C.T. and Touloumi, G. (2023). Joint modeling of longitudinal and competing-risk data using cumulative incidence functions for the failure submodels accounting for potential failure cause misclassification through double sampling. Biostatistics, 25, 80-97. Hoffmann, T.C., Montori, V.M. and Del Mar, C. (2014). The connection between evidence-based medicine and shared decision making. The Journal of the American Medical Association, 312, 1295-1296. Paige, E., Barrett, J., Pennells, L., Sweeting, M., Willeit, P., Di Angelantonio, E. et al. (2017). Use of repeated blood pressure and cholesterol measurements to improve cardiovascular disease risk prediction: An individual-participant-data meta-analysis. American Journal of Epidemiology, 186, 899-907. |
| References_xml | – reference: Hoffmann, T.C., Montori, V.M. and Del Mar, C. (2014). The connection between evidence-based medicine and shared decision making. The Journal of the American Medical Association, 312, 1295-1296. – reference: Yokota, I. and Matsuyama, Y. (2019). Dynamic prediction of repeated events data based on landmarking model: application to colorectal liver metastases data. BMC Medical Research Methodology, 19, 31. – reference: Lin, J. and Luo, S. (2022). Deep learning for the dynamic prediction of multivariate longitudinal and survival data. Statistics in Medicine, 41, 2894-2907. – reference: Wulfsohn, M.S. and Tsiatis, A.A. (1997). A joint model for survival and longitudinal data measured with error. Biometrics, 53, 330-339. – reference: Devaux, A., Genuer, R., Peres, K. and Proust-Lima, C. (2022). Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach. BMC Medical Research Methodology, 22, 188. – reference: Xie, Y., He, Z., Tu, W. and Yu, Z. (2020). Variable selection for joint models with time-varying coefficients. Statistical Methods in Medical Research, 29, 309-322. – reference: Putter, H. and van Houwelingen, H.C. (2022). Landmarking 2.0: Bridging the gap between joint models and landmarking. Statistics in Medicine, 41, 1901-1917. – reference: Li, Z., Chinchilli, V.M. and Wang, M. (2019). A Bayesian joint model of recurrent events and a terminal event. Biometrical Journal, 61, 187-202. – reference: Spolverato, G., Azzolina, D., Paro, A., Lorenzoni, G., Gregori, D., Poultsides, G. et al. (2022a) Dynamic prediction of survival after curative resection of gastric adenocarcinoma: A landmarking-based analysis. European Journal of Surgical Oncology, 48, 1025-1032. – reference: Zhu, Y., Huang, X. and Li, L. (2020). Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers. Biometrical Journal, 62, 1371-1393. – reference: Rizopoulos, D. and Ghosh, P. (2011). A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event. Statistics in Medicine, 30, 1366-1380. – reference: Nicolaie, M.A., van Houwelingen, J.C., de Witte, T.M. and Putter, H. (2013). Dynamic prediction by landmarking in competing risks. Statistics in Medicine, 32, 2031-2047. – reference: Andrinopoulou, E.-R., Rizopoulos, D., Takkenberg, J.J.M. and Lesaffre, E. (2017). Combined dynamic predictions using joint models of two longitudinal outcomes and competing risk data. Statistical Methods in Medical Research, 26, 1787-1801. – reference: van Houwelingen, H. and Putter, H. (2011). Dynamic Prediction in Clinical Survival Analysis. Chapman and Hall/CRC, Boca Raton, FL. – reference: Thomadakis, C., Meligkotsidou, L., Yiannoutsos, C.T. and Touloumi, G. (2023). Joint modeling of longitudinal and competing-risk data using cumulative incidence functions for the failure submodels accounting for potential failure cause misclassification through double sampling. Biostatistics, 25, 80-97. – reference: Tsiatis, A.A., Degruttola, V. and Wulfsohn, M.S. (1995). Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association, 90, 27-37. – reference: Heagerty, P.J. and Zheng, Y. (2005). Survival model predictive accuracy and ROC curves. Biometrics, 61, 92-105. – reference: Caillebotte, A., Kuhn, E. and Lemler, S. (2023). Estimation and variable selection in a joint model of survival times and longitudinal outcomes with random effects. arXiv:2306.16765. – reference: Park, K.Y. and Qiu, P. (2014). Model selection and diagnostics for joint modeling of survival and longitudinal data with crossing hazard rate functions. Statistics in medicine, 33, 4532-4546. – reference: Staplin, N.D., Kimber, A.C., Collett, D. and Roderick, P.J. (2015). Dependent censoring in piecewise exponential survival models. Statistical methods in medical research, 24, 325-341. – reference: Murray, J. and Philipson, P. (2023). Fast estimation for generalised multivariate joint models using an approximate EM algorithm. Computational Statistics and Data Analysis, 187, 107819. – reference: Steyerberg, E.W., Moons, K.G.M., van der Windt, D.A., Hayden, J.A., Perel, P., Schroter, S. et al. (2013). Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Medicine, 10, e1001381. – reference: Paige, E., Barrett, J., Stevens, D., Keogh, R.H., Sweeting, M.J., Nazareth, I. et al. (2018). Landmark models for optimizing the use of repeated measurements of risk factors in electronic health records to predict future disease risk. American Journal of Epidemiology, 187, 1530-1538. – reference: Wu, C., Li, L. and Li, R. (2020). Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers. Statistical Methods in Medical Research, 29, 3179-3191. – reference: Liang, M., Li, Z., Li, L., Chinchilli, V.M., Zhang, L. and Wang, M. (2023). Tackling dynamic prediction of death in patients with recurrent cardiovascular events. Statistics in Medicine, 42, 3487-3507. – reference: Murtaugh, P.A., Dickson, E.R., Van Dam, G.M., Malinchoc, M., Grambsch, P.M., Langworthy, A.L. et al. (1994). Primary biliary cirrhosis: prediction of short-term survival based on repeated patient visits. Hepatology, 20, 126-134. – reference: Andrinopoulou, E.-R., Rizopoulos, D., Takkenberg, J.J.M. and Lesaffre, E. (2014). Joint modeling of two longitudinal outcomes and competing risk data. Statistics in Medicine, 33, 3167-3178. – reference: Paige, E., Barrett, J., Pennells, L., Sweeting, M., Willeit, P., Di Angelantonio, E. et al. (2017). Use of repeated blood pressure and cholesterol measurements to improve cardiovascular disease risk prediction: An individual-participant-data meta-analysis. American Journal of Epidemiology, 186, 899-907. – reference: Hickey, G.L., Philipson, P., Jorgensen, A. and Kolamunnage-Dona, R. (2018). joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes. BMC Medical Research Methodology, 18, 50. – reference: Harhay, M.O., Gasparini, A., Walkey, A.J., Weissman, G.E., Crowther, M.J., Ratcliffe, S.J. et al. (2020). Assessing the Course of Organ Dysfunction Using Joint Longitudinal and Time-to-Event Modeling in the Vasopressin and Septic Shock Trial. Critical Care Explorations, 2, e0104. – reference: Kheirandish, M., Catanzaro, D., Crudu, V. and Zhang, S. (2022). Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes. Journal of the American Medical Informatics Association, 29, 900-908. – reference: Huang, X., Li, G., Elashoff, R.M. and Pan, J. (2011). A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects. Lifetime Data Analysis, 17, 80-100. – reference: Sweeting, M.J., Barrett, J.K., Thompson, S.G. and Wood, A.M. (2017). The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study. Statistics in Medicine, 36, 4514-4528. – reference: Murray, J. and Philipson, P. (2022). A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data. Computational Statistics and Data Analysis, 170, 107438. – reference: Lenain, R., Dantan, E., Giral, M., Foucher, Y., Asar, Ö., Naesens, M. et al. (2021). External validation of the DynPG for kidney transplant recipients. Transplantation, 105, 396-403. – reference: Yu, M., Taylor, J.M.G. and Sandler, H.M. (2008). Individual prediction in prostate cancer studies using a joint longitudinal survival-cure model. Journal of the American Statistical Association, 103, 178-187. – reference: Cekic, S., Aichele, S., Brandmaier, A.M., Köhncke, Y. and Ghisletta, P. (2021). A Tutorial for Joint Modeling of Longitudinal and Time-to-Event Data in R. Quantitative and Computational Methods in Behavioral Sciences, 1-40. – reference: Harshman, L.C., Xie, W., Bjarnason, G.A., Knox, J.J., MacKenzie, M., Wood, L. et al. (2012). Conditional survival of patients with metastatic renal-cell carcinoma treated with VEGF-targeted therapy: a population-based study. The Lancet Oncology, 13, 927-935. – reference: Skuladottir, H. and Olsen, J.H. (2003). Conditional survival of patients with the four major histologic subgroups of lung cancer in Denmark. Journal of Clinical Oncology, 21, 3035-3040. – reference: Spolverato, G., Capelli, G., Lorenzoni, G., Gregori, D., He, J., Popescu, I. et al. (2022b) Dynamic Prediction of Survival After Curative Resection of Intrahepatic Cholangiocarcinoma: A Landmarking-Based Analysis. Annals of Surgical Oncology, 29, 7634-7641. – reference: Anderson, J.R., Cain, K.C. and Gelber, R.D. (1983). Analysis of survival by tumor response. Journal of Clinical Oncology, 1, 710-719. – reference: Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. Chapman & Hall/CRC, Boca Raton, FL. – reference: Rizopoulos, D. (2011). Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics, 67, 819-829. – reference: van Houwelingen, H.C. (2007). Dynamic prediction by landmarking in event history analysis. Scandinavian Journal of Statistics, 34, 70-85. – reference: Andersen, P.K. and Liestøl, K. (2003). Attenuation caused by infrequently updated covariates in survival analysis. Biostatistics, 4, 633-649. – reference: 江村剛志，道前洋史 (2020). コピュラを用いた生存時間解析： 相関のあるエンドポイントとメタ分析の活用．統計数理， 68, 147-174. – reference: Fournier, M.-C., Foucher, Y., Blanche, P., Legendre, C., Girerd, S., Ladrière, M. et al. (2019). Dynamic predictions of long-term kidney graft failure: an information tool promoting patient-centred care. Nephrology Dialysis Transplantation, 34, 1961-1969. – reference: Rizopoulos, D., Papageorgiou, G. and Miranda Afonso, P. (2023). JMbayes2: Extended Joint Models for Longitudinal and Time-to-Event Data. URL https://drizopoulos.github.io/JMbayes2/index.html [accessed 10 December 2023] |
| SSID | ssj0000603397 ssib005627525 ssib029852174 |
| Score | 2.4068632 |
| SourceID | jstage |
| SourceType | Publisher |
| StartPage | 189 |
| Subtitle | ジョイントモデルとランドマークアプローチ |
| Title | 経時測定バイオマーカーを用いた生存時間アウトカムに対する動的予測 |
| URI | https://www.jstage.jst.go.jp/article/jjb/45/2/45_189/_article/-char/ja |
| Volume | 45 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| ispartofPNX | 計量生物学, 2024/11/30, Vol.45(2), pp.189-214 |
| journalDatabaseRights | – providerCode: PRVAFT databaseName: Colorado Digital library customDbUrl: eissn: 2185-6494 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000603397 issn: 0918-4430 databaseCode: KQ8 dateStart: 19920101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LaxRBEB5CRPAiPvFNDvZx4jz6eZKezYSgIAgJ5DbuzvQc9hBFkoPedjaHQDyIRjE5GBERMT5Ogoj4Z8bN4r-wuqdnd1w8JLk0tdXdVdVfzUx3NdvVjnM9yARVjCvwgMIuVm3icsKw65FOO00DP6dtk-3zDl1YwreWyfLU9M3Gv5bWVjuz6eP_nis5ileBB37Vp2QP4dmRUGAADf6FEjwM5YF8jGKGIoJ4C8UUCYF4oImIIwkcgmSMhERxiHiIhGeIAElcE6Oq2BJRq66KJjki0LoERpIbjo84toSYt1WaAKVzSPCGPQIJoMNaclAT1KrgfFKp9KxkzQGB8yiKa13CNuamCkpBjHZp7MEokkagBaG58kYx18brWjCMId40G34KJIW1X452KzVDzJlxEBQB2JF-JLUE6RvVROOpkRRmLPG4I9XadI1REnlVR2NzFIybNVEJawyaHDmJHJgxco6RWQHSRMZgFYXNUWgzmRmF1EO0xkRCA6XhArlRcwcowHXmSfvOGpcyJIkhWuYROwSixjktcFRzb9jnLsZWhzI8WAoSl-LqRup66qwygdpPRNCYB_3qXii7pAqqc8KTszWhwszW3c4sJrOjLv9kP7fvVgKNEkySQBfQMqn5-mxi0oUA6VjAKNVXmNy-28hPp3NwjyesQHCiQ_DRJqxHvTA0FzCNhlylMNam3RgbBsvTLgRr9R89zdpz8ZRz0gaNM7Ky5rQz1W2fcY5X18g-OuvcG357sr9d7H__NPiyU_aflsW7svhY9l-X_Z9lsWfKZ8OtD2VvveztDrd2B59fQfs_L5-XxduyeF_2N0yzN2Vvb_D1V9nbLovNweaL4c767x8bIPacszQfL7YWXHtzitv1Q_hIsjT3FCzmMQRYKmdZh-cQtyilcJZzlqksYBlRHs6JEn5GKXAgMkxF2AmZUoyE553plfsr6oIzk5E05TlLMc04bjO_LQg8gioL_TDnKk8vOrSCJnlQpcdJDuiyS0fteNk5MX4LrjjTqw_X1FWIDFY714z3_wI0Bdy4 |
| linkProvider | Colorado Alliance of Research Libraries |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E7%B5%8C%E6%99%82%E6%B8%AC%E5%AE%9A%E3%83%90%E3%82%A4%E3%82%AA%E3%83%9E%E3%83%BC%E3%82%AB%E3%83%BC%E3%82%92%E7%94%A8%E3%81%84%E3%81%9F%E7%94%9F%E5%AD%98%E6%99%82%E9%96%93%E3%82%A2%E3%82%A6%E3%83%88%E3%82%AB%E3%83%A0%E3%81%AB%E5%AF%BE%E3%81%99%E3%82%8B%E5%8B%95%E7%9A%84%E4%BA%88%E6%B8%AC&rft.jtitle=%E8%A8%88%E9%87%8F%E7%94%9F%E7%89%A9%E5%AD%A6&rft.au=%E5%9D%82%E5%B7%BB%2C+%E9%A1%95%E5%A4%AA%E9%83%8E&rft.au=%E6%A8%AA%E7%94%B0%2C+%E5%8B%B2&rft.au=%E3%82%A2%E3%83%AB%E3%82%A2%E3%83%AA%E3%82%A2%E3%82%B7%E3%83%BC%2C+%E3%82%89%E3%82%8B%E3%81%B3&rft.au=%E5%A4%A7%E5%BA%AD%2C+%E5%B9%B8%E6%B2%BB&rft.date=2024-11-30&rft.pub=%E6%97%A5%E6%9C%AC%E8%A8%88%E9%87%8F%E7%94%9F%E7%89%A9%E5%AD%A6%E4%BC%9A&rft.issn=0918-4430&rft.eissn=2185-6494&rft.volume=45&rft.issue=2&rft.spage=189&rft.epage=214&rft_id=info:doi/10.5691%2Fjjb.45.189&rft.externalDocID=article_jjb_45_2_45_189_article_char_ja |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0918-4430&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0918-4430&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0918-4430&client=summon |