Journal of Shandong University (Health Sciences) ›› 2020, Vol. 58 ›› Issue (9): 71-76.doi: 10.6040/j.issn.1671-7554.0.2020.0555

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Prediction of the prognosis of 312 cirrhosis patients using the joint models and database

XIAO Yufei, FENG Jianing, WANG Xiaoxuan, MAO Qian, SHI Fuyan, WANG Suzhen   

  1. School of Public Health, Weifang Medical University, Weifang 261053, Shandong, China
  • Online:2020-09-10 Published:2020-08-30

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Abstract: Objective To explore the dynamic prediction efficiency of longitudinal survival data after slope term of longitudinal monitoring variables was added on the standard joint model. Methods Based on the dataset of primary biliary cirrhosis(PBC2)in the R 3.6.2 software of JM package, a standard joint model and a time-varying slope parameterized joint model were constructed, whose fitting results were analyzed. Their prediction efficiency on the survival rate of cirrhosis patients were compared and the individual survival rate was predicted. Results The standard joint model showed that with each unit of log(serBilir)increased longitudinally, the risk of death increased by 3.852 0 times(95%CI: 3.152 5-4.706 3). The time-varying slope parameterized joint model showed that with each unit of log(serBilir)increased longitudinally, the risk of death increased by 3.135 2 times(95%CI: 2.433 2-4.039 8). If the slope of the longitudinal trajectory increased by one unit, the risk of death increased by 17.431 9 times(95%CI: 2.399 6-126.633 9). The slope of log(serBilir)longitudinal trajectory was highly correlated with the risk of death(P<0.000 1). Both models were more accurate in predicting the 4-year survival rate than predicting the 2-year survival rate. The area under the ROC curve(AUC)of the time-varying slope parameterized joint model(0.714 2-0.831 1)was smaller than that of the standard joint model(0.645 0-0.836 1), and the prediction performance was more stable. The time-varying slope parameterized joint model predicted the 4-year survival rate was 0.850 3(95%CI: 0.481 0-0.963 9)for 1 randomized patient. Conclusion Both the standard joint model and time-varying slope parameterized joint model can be used to analyze the correlation between longitudinal monitoring indexes and survival outcomes in longitudinal survival data. The time-varying slope parameterized joint model not only has the function of the standard joint model, but also takes into account the influence of the trajectory slope of longitudinal monitoring variables on survival outcomes. Therefore, it has higher prediction efficiency than the standard joint model.

Key words: Standard joint model, Slope parameterization, Longitudinal data, Survival analysis, Prediction

CLC Number: 

  • R18
[1] 赵悦. 纵向数据与生存数据联合模型的变量选择[D]. 长春: 东北师范大学, 2019.
[2] 张慧敏. 基于联合模型的纵向和生存数据统计方法探讨[D]. 南京: 东南大学, 2019.
[3] 孙申. 基于一组肝硬化临床实验数据的两个纵向和生存数据的联合模型及其比较研究[D]. 昆明: 云南师范大学, 2019.
[4] Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error [J]. Biometrics, 1997, 53(1): 330-339.
[5] Tsiatis AA, Degruttola V, Wulfsohn MS. Modeling the relationship of survival to longitudinal data measured with error: applications to survival and CD4 counts in patients with AIDS [J]. J Am Stat Assoc, 1995, 90(429):27-37.
[6] 范永君, 刘启贵, 刘珂华, 等. 尿酸的动态变化与代谢综合征的关联研究[J]. 中国卫生统计, 2019, 36(4): 507-510. FAN Yongjun, LIU Qigui, LIU Kehua, et al. The correlation between dynamic changes of uric acid and metabolic syndrome [J]. Chinese Journal of Health Statistics, 2019, 36(4): 507-510.
[7] Rizopoulos D, Murawska M, Andrinopoulou ER, et al. Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking [J]. Biom J, 2017, 59(6): 1261-1276.
[8] 严国义. 纵向数据与生存数据的半参数联合模型研究[D]. 武汉: 武汉大学, 2013.
[9] Rizopoulos D, Hatfield LA, Carlin BP, et al. Combining dynamic predictions from joint models for longitudinal and time-to-event data using Bayesian model averaging [J]. J Am Stat Assoc, 2014, 109(508):1-29.
[10] Huang Y, Yan C, Xing D, et al. Jointly modeling event time and skewed-longitudinal data with missing response and mismeasured covariate for AIDS studies [J]. J Biopharm Stat, 2015, 25(4): 670-694.
[11] 王策. 纵向和生存数据的一个联合建模方法研究及其在艾滋病疗效评估中的应用[D]. 昆明: 云南师范大学, 2015.
[12] 王旭霞. 基于联合模型的认知功能和社会功能对阿尔茨海默病风险预测研究[D]. 太原:山西医科大学, 2019.
[13] Li K, Luo S. Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimers disease [J]. Stat Methods Med Res, 2019, 28(2): 327-342.
[14] Rizopoulos D, Biometrics. Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data [J]. Biometrics, 2011,67(3): 819-829.
[15] 王一茸, 韦程东, 岑泰林, 等. 基于纵向生存数据联合模型的肝硬化数据应用研究[J]. 广西师范学院学报(自然科学版), 2018, 35(2): 31-36. WANG Yirong, WEI Chengdong, CEN Tailin, et al. Liver cirrhosis data application research based on the joint model of longitudinal data and survival[J]. Journal of Nanning Teachers Education University(Natural Science Edition), 2018, 35(2): 31-36.
[16] 孙宏鸽, 刘启贵, 王晓蓉, 等. 基于联合模型的谷丙和谷草转氨酶动态变化与代谢综合征的关联研究[J]. 中国卫生统计, 2018, 35(2): 181-185. SUN Hongge, LIU Qigui, WANG Xiaorong, et al. A longitudinal analysis on the relationship between dynamic changes of alanine aminotransferase and a longitudinal analysis on the relationship between dynamic changes of alanine aminotransferase and metabolic syndrome based on the joint model[J]. Chinese Journal of Health Statistics, 2018, 35(2): 181-185.
[17] 李淞淋, 魏戌, 易丹辉. 基于纵向和生存时间结局的联合模型方法及应用[J]. 世界科学技术-中医药现代化, 2019, 21(3): 366-373. LI Songlin, WEI Xu, YI Danhui. Methodology and application of joint models based on longitudinal endpoints and time to events [J]. Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology, 2019, 21(3): 366-373.
[18] Ibrahim JG, Chu H, Chen LM. Basic concepts and methods for joint models of longitudinal and survival data [J]. J Clin Oncol, 2010, 28(16): 2796-2801.
[19] Garre FG, Zwinderman AH, Geskus RB, et al. A joint latent class changepoint model to improve the prediction of time to graft failure [J]. J R Stat Soc Series B Stat Methodol, 2008, 171(1): 299-308.
[20] 池铭. 纵向项目响应与有治愈的生存时间的联合建模[D]. 大连: 大连理工大学, 2019.
[21] 翟映红, 陈琪, 韩贺东, 等. 联合模型介绍及在医学研究中的应用[J]. 中华流行病学杂志, 2019, 40(11):1456-1460.
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