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
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