Improving the Lung Cancer Clinical Trial Development by Incorporating Competing Risk Factors

Abstract

Introduction. Distinct from other diseases, as cancer progresses, both the symptoms and treatments evolve, resulting in a complex, time-dependent relationship. Many competing risk factors influence the outcome of cancer. An improved method was used to evaluate the data from 6 non-small-cell lung cancer (NSCLC) clinical trials combined in our center since 2016 to deal with the bias caused by competing risk factors. Material and Methods. Data of 118 lung cancer patients were collected from 2016 to 2020. Fine and Gray’s model for competing risk was used to evaluate survival of different treatment group compares with the classic survival analysis model. Results. Immunotherapy had better progression-free survival than chemotherapy. (HR: 0.62, 95% CI: 0.41-0.95, $p=0.0260$ ). However, there were no significant differences in patients who withdrew due to treatment-related adverse events from different groups. ( $Z=0.0508$ , $p=0.8217$ ). The PD-1/PD-L1 inhibitors in our study did not significantly improve overall survival compared with chemotherapy (HR:0.77, 95% CI:0.48-1.24, $p=0.2812$ ), estimated 1-year overall survival rates were 55% and 46%, and 3-year overall survival rates were 17% and 10%, respectively. Conclusion. When the outcome caused by competing risk exists, the corresponding competing risk model method should be adopted to eliminate the bias caused by the classic survival analysis.