Restricted mean survival time regression model with time‐dependent covariates
In clinical or epidemiological follow‐up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the propo...
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| Published in: | Statistics in medicine Vol. 41; no. 21; pp. 4081 - 4090 |
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| Format: | Journal Article |
| Language: | English |
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20-09-2022
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| Abstract | In clinical or epidemiological follow‐up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based on the RMST are indirect or direct models of the RMST and baseline covariates. However, time‐dependent covariates are becoming increasingly common in follow‐up studies. Based on the inverse probability of censoring weighting (IPCW) method, we developed a regression model of the RMST and time‐dependent covariates. Through Monte Carlo simulation, we verified the estimation performance of the regression parameters of the proposed model. Compared with the time‐dependent Cox model and the fixed (baseline) covariate RMST model, the time‐dependent RMST model has a better prediction ability. Finally, an example of heart transplantation was used to verify the above conclusions. |
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| AbstractList | In clinical or epidemiological follow‐up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based on the RMST are indirect or direct models of the RMST and baseline covariates. However, time‐dependent covariates are becoming increasingly common in follow‐up studies. Based on the inverse probability of censoring weighting (IPCW) method, we developed a regression model of the RMST and time‐dependent covariates. Through Monte Carlo simulation, we verified the estimation performance of the regression parameters of the proposed model. Compared with the time‐dependent Cox model and the fixed (baseline) covariate RMST model, the time‐dependent RMST model has a better prediction ability. Finally, an example of heart transplantation was used to verify the above conclusions. |
| Author | Yuan, Hao Hou, Yawen Zhang, Chengfeng Huang, Baoyi Wu, Hongji Chen, Zheng |
| Author_xml | – sequence: 1 givenname: Chengfeng surname: Zhang fullname: Zhang, Chengfeng organization: Southern Medical University – sequence: 2 givenname: Baoyi surname: Huang fullname: Huang, Baoyi organization: Southern Medical University – sequence: 3 givenname: Hongji surname: Wu fullname: Wu, Hongji organization: Southern Medical University – sequence: 4 givenname: Hao surname: Yuan fullname: Yuan, Hao organization: Southern Medical University – sequence: 5 givenname: Yawen surname: Hou fullname: Hou, Yawen email: thouyw@jnu.edu.cn organization: Jinan University – sequence: 6 givenname: Zheng orcidid: 0000-0003-3201-2666 surname: Chen fullname: Chen, Zheng email: zheng-chen@hotmail.com organization: Southern Medical University |
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| CitedBy_id | crossref_primary_10_1016_j_annonc_2024_05_544 crossref_primary_10_3389_fonc_2024_1352111 crossref_primary_10_1111_biom_13891 crossref_primary_10_1016_j_cct_2024_107440 crossref_primary_10_1109_JBHI_2023_3292475 |
| Cites_doi | 10.1002/sim.8206 10.1002/sim.5452 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 10.1002/sim.8086 10.1093/biomet/90.1.15 10.1093/aje/kwab235 10.1111/j.0006-341X.2001.01030.x 10.1080/01621459.1987.10478555 10.1111/biom.13414 10.1080/10543406.2020.1814799 10.1177/0962280209105020 10.1111/j.1541-0420.2010.01503.x 10.1093/biomet/asm036 10.1016/j.cmpb.2021.106155 10.1093/jnci/djx179 10.1093/biostatistics/kxt050 10.1161/CIRCOUTCOMES.119.005918 10.1002/pst.2004 10.1002/sim.2673 10.1093/biomet/78.3.691 10.1007/s10985-017-9391-6 10.1007/s10985-004-4771-0 10.1016/j.healun.2018.07.022 10.1053/j.ajkd.2017.01.004 10.1002/sim.4274 10.1080/01621459.1998.10473722 10.1080/01621459.1977.10479903 |
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| SubjectTerms | inverse probability of censoring weighting Medical research Medical statistics restricted mean survival time Statistical analysis survival analysis time‐dependent covariates |
| Title | Restricted mean survival time regression model with time‐dependent covariates |
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