Cancer Res Treat.  2022 Oct;54(4):996-1004. 10.4143/crt.2021.902.

The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer

  • 1Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
  • 2Department of Statistics and Institute of Applied Statistics, Jeonbuk National University, Jeonju, Korea
  • 3Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea
  • 4Department of Applied Statistics, University of Suwon, Hwaseong, Korea


The illness-death model (IDM) is a comprehensive approach to evaluate the relationship between relapse and death. This study aimed to illustrate the value of the IDM for identifying risk factors and evaluating predictive probabilities for relapse and death in patients with non–small cell lung cancer (NSCLC) in comparison with the disease-free survival (DFS) model.
Materials and Methods
We retrospectively analyzed 612 NSCLC patients who underwent a curative operation. Using the IDM, the risk factors and predictive probabilities for relapse, death without relapse, and death after relapse were simultaneously evaluated and compared to those obtained from a DFS model.
The IDM provided more detailed risk factors according to the patient’s disease course, including relapse, death without relapse, and death after relapse, in patients with resected lung cancer. In the IDM, history of malignancy (other than lung cancer) was related to relapse and smoking history was associated with death without relapse; both were indistinguishable in the DFS model. In addition, the IDM was able to evaluate the predictive probability and risk factors for death after relapse; this information could not be obtained from the DFS model.
Compared to the DFS model, we found that the IDM provides more comprehensive information on transitions between states and disease stages and provides deeper insights with respect to understanding the disease process among lung cancer patients.


Non-small cell lung carcinoma; Prognosis; Disease-free survival; Risk factors


  • Fig. 1 Flow diagram of patient inclusion and exclusion.

  • Fig. 2 Comparison of disease-free survival and illness-death models and their progression paths and counts for lung cancer patients.

  • Fig. 3 Predictive probabilities for an illustrative patienta) with the sample median or mode for each risk factor presented according to TNM stage (1A [A, B], 2A [C, D], 3A [E, F]), as estimated from the illness-death model (left) and the disease-free survival model (right). a)This patient’s risk factors were set to age=65, cancer type=adenocarcinoma, sex=male, treatment method=operation only, history of malignancy other than lung cancer=no, smoking history=previous or current smoker, nodule location=upper lobes and middle lobes, and operation method=lobectomy.



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