Construction of a competing risk model for disease-free survival of patients with non-muscle invasive bladder cancer / 现代泌尿外科杂志

【Objective】 To construct an easy-to-use individual survival prognostic tool based on competing risk analyses to predict the risk of 1-, 2- and 3- year recurrence for patients with non-muscle invasive bladder cancer (NMIBC). 【Methods】 The follow-up data of 419 NMIBC patients were obtained. The patients were randomly divided into training cohort (n=293) and validation cohort (n=126). The variables included age at diagnosis, sex, history of smoking, tumor number, tumor size, histolo-gic grade, pathological stage, and bladder perfusion drug. The cumulative incidence function (CIF) of recurrence was estimated using all variables in the training cohort and potential prognostic variables were determined with Gray’s test. The Fine-Gray subdistribution proportional hazard approach was used as a multivariate competitive risk analysis to identify independent pro-gnostic variables. A competing risk nomogram was developed to predict the recurrence. The performance of the competing risk model was evaluated with the area under the receiver operating characteristic curve (AUC), calibration curve, and Brier score. 【Results】 Five independent prognostic factors including age, number of tumors, tumor size, histologic grade and pathological stage were used to construct the competing risk model. In the validation cohort, the AUC of 1-, 2- and 3- year recurrence were 0.895 (95%CI: 0.831-0.959), 0.861(95%CI: 0.774-0.948) and 0.827(95%CI: 0.721-0.934), respectively, indicating that the model had a high predictive performance. 【Conclusion】 We successfully constructed a competing risk model to predict the risk of 1-, 2- and 3-year recurrence for NMIBC patients. It may help clinicians to improve the postoperative management of patients.

Survival status and influencing factors of HIV/AIDS on highly active anti-retrovial therapy in Shandong province / 中华流行病学杂志

Objective: To understand the survival status and influencing factors for HIV/AIDS patients on highly active anti-retroviral therapy (HAART) in Shandong province. Methods: Both Kaplan-Meier (K-M) method and cumulative incidence function (CIF) were used to calculate the cumulative incidence of AIDS-related death respectively, and Fine-Gray model was used to identify the influencing factors related to survival time. Results: Through K-M method, a higher AIDS-related cumulated death rate than the CIF, was estimated. Among all the HIV/AIDS patients who initiated HAART from 2003 to 2015 in Shandong, 5 593 of them met the inclusion criteria. The cumulative incidence rate for AIDS-related death was 3.08% in 1 year, 4.21% in 3 years, 5.37% in 5 years, and 7.59% in 10 years respectively by CIF. Results from the F-G analysis showed that HIV/AIDS patients who were on HAART, the ones who had college degree or above (HR=0.40, 95%CI: 0.24-0.65) were less likely to die of AIDS-associated diseases. However, HIV/AIDS patients who were on HAART and living in the western areas of Shandong (HR=1.33, 95%CI: 1.01-1.89), diagnosed by medical institutions (HR=1.39, 95%CI: 1.06-1.80), started to receive care ≥1 year after diagnosis (HR=2.02, 95%CI: 1.30-3.15), their CD(4) cell count less than 200 cells/μl (HR=3.41, 95%CI: 2.59-4.59) at the time of diagnosis, with NVP in antiviral treatment (ART) regime (HR=1.36, 95%CI: 1.03-1.88), at Ⅲ/Ⅳ clinical stages (HR=2.61, 95%CI: 1.94-3.53) and CD(4) cell count less than 350 cells/μl (HR=5.48,95%CI: 2.32-12.72) at initiation of HAART ect., were more likely to die of AIDS-associated diseases. Conclusions: With the existence of competing risks, the cumulative incidence rate for AIDS-related death was overestimated by K-M, suggesting that competing risk models should be used in the survival analysis. Measures as early diagnoses followed by timely care and early HAART could end up with the reduction of AIDS-related death.

Survival status and influencing factors of HIV/AIDS on highly active anti-retrovial therapy in Shandong province / 中华流行病学杂志

Objective To understand the survival status and influencing factors for HIV/AIDS patients on highly active anti-retroviral therapy (HAART) in Shandong province.Methods Both Kaplan-Meier (K-M) method and cumulative incidence function (CIF) were used to calculate the cumulative incidence of AIDS-related death respectively,and Fine-Gray model was used to identify the influencing factors related to survival time.Results Through K-M method,a higher AIDS-related cumulated death rate than the CIF,was estimated.Among all the HIV/AIDS patients who initiated HAART from 2003 to 2015 in Shandong,5 593 of them met the inclusion criteria.The cumulative incidence rate for AIDS-related death was 3.08％ in 1 year,4.21％ in 3 years,5.37％ in 5 years,and 7.59％ in 10 years respectively by CIF.Results from the F-G analysis showed that HIV/AIDS patients who were on HAART,the ones who had college degree or above (HR=0.40,95％CI:0.24-0.65) were less likely to die of AIDS-associated diseases.However,HIV/AIDS patients who were on HAART and living in the western areas of Shandong (HR=1.33,95％CI:1.01-1.89),diagnosed by medical institutions (HR=1.39,95％CI:1.06-1.80),started to receive care ≥1 year after diagnosis (HR=2.02,95％CI:1.30-3.15),their CD,cell count less than 200 cells/μl (HR=3.41,95％CI:2.59-4.59) at the time of diagnosis,with NVP in antiviral treatment (ART) regime (HR=1.36,95％CI:1.03-1.88),at Ⅲ/Ⅳ clinical stages (HR=2.61,95％CI:1.94-3.53) and CD4 cell count less than 350 cells/μl (HR=5.48,95％CI:2.32-12.72) at initiation of HAART ect.,were more likely to die of AIDS-associated diseases.Conclusions With the existence of competing risks,the cumulative incidence rate for AIDS-related death was overestimated by K-M,suggesting that competing risk models should be used in the survival analysis.Measures as early diagnoses followed by timely care and early HAART could end up with the reduction of AIDS-related death.

Level of education and mortality after radical prostatectomy / 亚洲男科学杂志(英文版)

Estimating the risk of competing mortality is of importance in men with early prostate cancer to choose the most appropriate way of management and to avoid over-or under-treatment. In this study, we investigated the impact of the level of education in this context. The study sample consisted of 2630 patients with complete data on level of education (college, university degree, master craftsmen, comparable profession, or others), histopathological tumor stage (organ confined or extracapsular), lymph node status (negative or positive), and prostatectomy specimen Gleason score (<7, 7, or 8-10) who underwent radical prostatectomy between 1992 and 2007. Overall, prostate cancer-specific, competing, and second cancer-related mortalities were study endpoints. Cox proportional hazard models for competing risks were used to study combined effects of the variables on these endpoints. A higher level of education was independently associated with decreased overall mortality after radical prostatectomy (hazard ratio [HR]: 0.75, 95% confidence interval [95% CI]: 0.62-0.91, P = 0.0037). The mortality difference was attributable to decreased second cancer mortality (HR: 0.59, 95% CI: 0.40-0.85, P = 0.0052) and noncancer mortality (HR: 0.73, 95% CI: 0.55-0.98, P = 0.0345) but not to differences in prostate cancer-specific mortality (HR: 1.16, 95% CI: 0.79-1.69, P = 0.4536 in the full model). In conclusion, the level of education might serve as an independent prognostic parameter supplementary to age, comorbidity, and smoking status to estimate the risk of competing mortality and to choose optimal treatment for men with early prostate cancer who are candidates for radical prostatectomy.

A new perspective of survival data on clinical epidemiology: introduction of competitive risk model / 中华流行病学杂志

Competing risks occur frequently in the analysis of survival data that should be dealt with competing risk models.Competing risk is an event whose occurrence precludes the occurrence of the primary event of interest.Previous commonly used Kaplan-Meier method tends to overestimate the cumulative survival functions,while the traditional Cox proportional hazards model falsely evaluates the effects of covariates on the hazard related to the occurrence of the event.There are few domestic reports mentioning the concept,application and methodology of competing risk model as well as the implementation procedures or resolution of model conditions and parameters.The current work aims to explain the core concept and methodology of the competing risk model and to illustrate the process of analysis on cumulative incidence rate,using both the cause-specific hazard function model and the sub-distribution hazard function model.Software macro code in SAS 9.4 is also provided to assist clinical researchers to further understand the application of the model so to properly analyze the survival data.

A new perspective of survival data on clinical epidemiology: introduction of competitive risk model / 中华流行病学杂志

Competing risks occur frequently in the analysis of survival data that should be dealt with competing risk models.Competing risk is an event whose occurrence precludes the occurrence of the primary event of interest.Previous commonly used Kaplan-Meier method tends to overestimate the cumulative survival functions,while the traditional Cox proportional hazards model falsely evaluates the effects of covariates on the hazard related to the occurrence of the event.There are few domestic reports mentioning the concept,application and methodology of competing risk model as well as the implementation procedures or resolution of model conditions and parameters.The current work aims to explain the core concept and methodology of the competing risk model and to illustrate the process of analysis on cumulative incidence rate,using both the cause-specific hazard function model and the sub-distribution hazard function model.Software macro code in SAS 9.4 is also provided to assist clinical researchers to further understand the application of the model so to properly analyze the survival data.

Competing risk model based study of outcomes of mild cognitive impairment of seniors / 中华流行病学杂志

Objective To introduce the competing risk model into outcome prediction of mild cognitive impairment (MCI) of seniors and to explore influencing factors for the prognosis of MCI to Alzheimer' s disease (AD).Methods Data were collected from six follow-up visits to 600 seniors from communities in Taiyuan city,which were conducted at an interval of six months from October 2010 to May 2013.MCI state was defined as the transient state,AD and death before AD as two absorbing states (death before AD in which was regarded as a competing risk event),building the competing risk model to identify the model parameters,and to explore influencing factors on MCI prognosis to AD.In the meantime,the 3-year MCI-AD transition probability was estimated based on the multi-state Markov model.Results Based on screening with the multivariate competing risk model analysis,factors such as higher age (estimate HR=1.56,95％CI:1.01-2.39),female gender (HR=1.72,95％CI:1.02-2.92),higher education(HR=0.64,95％CI:0.41-1.00),reading frequently (HR=0.57,95％CI:0.32-0.99),hypertension (HR=3.43,95％CI:1.08-10.85) and high SBP(HR=1.67,95％CI:1.04-2.66),were statistically significant for transition from MCI to AD in three years.3-year MCI-AD transition probability was 10.7％(95％CI:8.6％-13.2％).Conclusion Age,gender,education,reading and blood pressure were the influencing factors for the prognosis of MCI to AD.Competing risk model was advantageous in studying longitudinal data with multiple potential outcomes.

Competing risk: An illustration with aspiration pneumonia in head and neck cancer patients undergoing radical radiotherapy: A biostatistician’s perspective.

Interest in survival analysis is to look and capture the information about the occurrence of events, i.e., death. In human life different types of events may happen at the same time. Sometimes, few events completely interrupt or make subtle changes on the occurrence of an event of interest. The method to capture information about the specific event of interest along with other events is known as competing risk modeling. This paper is dedicated to explore the application of competing risk model in oncology practice. It is aimed in near future that more and more survival analysis will be performed through application of competing risk modeling instead of traditional survival analysis to generate robust statistical inference.