2021
DOI: 10.1177/1073274821989316
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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 patie… Show more

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Cited by 19 publications
(25 citation statements)
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References 55 publications
(76 reference statements)
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“…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%.…”
Section: Discussionsupporting
confidence: 41%
“…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%.…”
Section: Discussionsupporting
confidence: 41%
“…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.…”
Section: The Data-mining Process and Examples Of Its Application Using Common Public Databasesmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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 ].…”
Section: Introductionmentioning
confidence: 99%