Competitive Risk Analysis of Prognosis in Patients With Cecum Cancer: A Population-Based Study

Abstract

Background: The presence of competing risks means that the results obtained using the classic Cox proportional-hazards model for the factors affecting the prognosis of patients diagnosed with cecum cancer (CC) may be biased. Objective: The purpose of this study was to establish a competitive risk model for patients diagnosed with CC to evaluate the relevant factors affecting the prognosis of patients, and to compare the results with the classical COX proportional risk model. Methods: We extracted data on patients diagnosed with CC registered between 2004 and 2016 in the Surveillance, Epidemiology, and End Results (SEER) database. The univariate analysis utilized the cumulative incidence function and Gray’s test, while a multivariate analysis was performed using the Fine-Gray, cause-specific (CS), and Cox proportional-hazards models. Results: The 54463 eligible patients diagnosed with CC included 24387 who died: 12087 from CC and 12300 from other causes. The multivariate Fine-Gray analysis indicated that significant factors affecting the prognosis of patients diagnosed with CC include: age, race, AJCC stage, differentiation grade, tumor size, surgery, radiotherapy, chemotherapy and regional lymph nodes metastasis. Due to the presence of competitive risk events, COX model results could not provide accurate estimates of effects and false-negative results occurred. In addition, COX model misestimated the direction of association between regional lymph node metastasis and cumulative risk of death in patients diagnosed with CC. Competitive risk models tend to be more advantageous when analyzing clinical survival data with multiple endpoints. Conclusions: The present study can help clinicians to make better clinical decisions and provide patients diagnosed with CC with better support.

Detection of secondary upper gastrointestinal tract cancer during follow‐up esophagogastroduodenoscopy after gastrectomy for gastric cancer

“…In contrast, the competitive risk model enables the identification of incidence rates of and risk factors for secondary cancer in all patients, including those with advanced cancer. 18,19 In the present study, the 5-year cumulative incidence rates of secondary laryngeal and esophageal cancers were 1.3%.…”

Data mining in clinical big data: the frequently used databases, steps, and methodological models

“…Wu et al [ 101 ] noted that traditional survival-analysis methods often ignored the influence of competitive risk events, such as suicide and car accident, on outcomes, leading to deviations and misjudgements in estimating the effect of risk factors. They used the SEER database, which offers cause-of-death data for cancer patients, and a competitive risk model to address this problem according to the following process: (1) data were obtained from the SEER database; (2) demography, clinical characteristics, treatment modality, and cause of death of cecum cancer patients were extracted from the database; (3) patient data were deleted when there were no demographic, clinical, therapeutic, or cause-of-death variables; (4) Cox regression and two kinds of competitive risk models were applied for survival analysis; (5) the results were compared between three different models; and (6) the results revealed that for survival data with multiple endpoints, the competitive risk model was more favourable.…”

Competing risk of the specific mortality among Asian-American patients with prostate cancer: a surveillance, epidemiology, and end results analysis

“…Pettersson et al [ 13 ] used the Cox regression to investigate the association between age at diagnosis and prognosis for PCa patients, the result displayed that PCa had more aggressive and higher mortality for older men. But actually, death from PCa was only one of the death causes, and death caused by other diseases or traffic accidents would exist as well [ 14 , 15 ], it must be admitted that Cox proportional hazard model tended to make the outcomes’ risk higher, causing bias [ 15 ]. A relatively important issue is to accurately determine which factors affecting the survival and prognosis of PCa patients for Asian-Americans.…”

“…A relatively important issue is to accurately determine which factors affecting the survival and prognosis of PCa patients for Asian-Americans. In recent years, there were studies pointed that compared with the Cox model, the competing-risk model could better estimate the risk of major outcomes of benefit when one or more competitive risks are existed, and evaluated the factors of prognosis by competing-risk model would be more helpful to identify the associated risk factors accurately [ 15 – 17 ].…”