Dynamic prediction and prognostic analysis of patients with cervical cancer: a landmarking analysis approach

• Published in: Annals of epidemiology 2020-04, Vol.44, p.45-51
• Main Authors: Yang, Zijing, Hou, Yawen, Lyu, Jingjing, Liu, Di, Chen, Zheng
• Format: Article
• Language: English
• Subjects: Cervical cancer; Dynamic prediction; Landmarking; Personalized prediction; Time-varying effect
• ISSN: 1047-2797; 1873-2585
• DOI: 10.1016/j.annepidem.2020.01.009

Providing up-to-date information on patient prognosis is important in determining the optimal treatment strategies. The currently available prediction models, such as the Cox model, are limited to making predictions from baseline and do not consider the time-varying effects of covariates. A total of 1501 cervical cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database were included. We introduced three landmark dynamic prediction models (models 1–3) that explore the dynamic effects of prognostic factors to obtain 5-year dynamic survival rate predictions at different prediction times. The performances of these models were evaluated by Harrell's C-index and the Brier score using cross-validation. Some variables did not meet the proportional hazards assumption, indicating that the constant hazard ratios were unreliable. Model 3, which showed the best performance for prediction, was selected as the final model. Significant time-varying effects were observed for age at diagnosis, International Federation of Gynecology and Obstetrics (FIGO) stage, lymph node metastasis, and histological subtypes. Three patients were as examples used to illustrate how the predicted probabilities change at different prediction times during follow-up. Model 3 can effectively incorporate covariates with time-varying effects and update the probability of surviving an additional 5 years at different prediction times. The use of the landmark approach may provide evidence for clinical decision making by updating personalized information for patients. •A landmark dynamic prediction model predicted survival of cervical cancer patients.•This model can offer individualized risks for patients based on the prediction time.•This model can provide better evidence for clinical decision making.•Time-varying effects based on age at diagnosis, FIGO stage, and so on were observed.