Affiliation:
1. Department of Ophthalmology The First Affiliated Hospital of Jinan University Guangzhou China
2. Ophthalmic Center, The Second Affiliated Hospital Guangzhou Medical University Guangzhou China
3. Department of Ophthalmology The First People's Hospital of Chenzhou Chenzhou China
4. Department of Clinical Research The First Affiliated Hospital of Jinan University Guangzhou China
Abstract
AbstractBackgroundAccurate prognostic factors for primary ocular adnexal lymphoma (POAL) are scarce. Survival models and prognostic factors derived without considering competing risk factors suffer from major statistical errors. This study aimed to accurately assess prognostic factors in POAL patients using competing risk models, and compare this to the traditional COX proportional hazards model.MethodsThis retrospective study utilised data from the Surveillance, Epidemiology, and End Results (SEER) program 2010–2015 and included patients with B‐cell POAL. The cumulative incidence function and Gray's test for cause‐specific survival were calculated as univariate analysis. The competing risk models were a Fine‐Gray subdistribution hazard model and a cause‐specific model, and a traditional COX model was employed as a multivariate analysis.ResultsThis study enrolled 846 eligible patients with POAL: 60 patients (7.09%) died from POAL and 123 patients (14.54%) died from other causes. Multivariate competing risk models indicated that age, laterality, histology subtype, the 7th edition of American Joint Committee on Cancer stage T, and radiotherapy were independent predictors for cause‐specific survival of patients with POAL. There was high consistency between the two competing risk models. The COX model made several misestimations on the statistical significance and hazard ratios of prognostic factors.ConclusionsThis study established competing risk models as a method to assess POAL prognostic factors, which was more accurate than traditional methods that do not consider competing risk elements.
Funder
Natural Science Foundation of Guangdong Province
National Natural Science Foundation of China