Confidence Intervals for Cumulative Incidence Function with Competing Risks Data / 中国卫生统计

ABSTRACT

Objective The cumulative incidence function (CIF) is an important descriptive indicator for competing risk data in medical follow-up study.However,the upper and lower limits of the classic confidence interval (CI) of CIF may be exclusive the boundaries.In this paper,the CI estimators based on five different transformations and their performances are studied.Methods The CIs of CIF are constructed based on the linear (classical),log,log (-log),arcsine and logit transformation,respectively.Through the simulation study,the average deviations of the false coverage probabilities for all CIs are comprehensively investigated by the ANOVA technology.Results The simulation results show that the CIs based on linear and arcsine transformation have a large positive deviation.Log transformation is prone to fluctuations and has a minimum negative deviation,only log (-log) transformation is closest to the expected constant 0,and most robust and reliable.Conclusion Combined with the simulation results and example,CIs base on linear and log transformation are easy to have wide range and unstable performance,and can not overcome the bounds being negative or above 1;the arcsine and logit is slightly fluctuated,but their performances are relatively balanced;only performance of log(-log) is the most robust and reliable.