BIAS REDUCTION IN ESTIMATING A CONCORDANCE FOR CENSORED TIME-TO-EVENT RESPONSES

Abstract

ABSTRACT Evaluating the relationship between a response variable and explanatory variables is an important part of establishing better statistical models. Concordance provides a measure for this relationship. In this study, we estimate the concordance for time to-event data, which often occur in medical sciences. In general, censored cases are observed in the data set. Moreover, the distribution of the censoring time usually varies among studies, even when a target population is the same. Hence, it is desirable that we reduce the effect of the censoring distribution when estimating the concordance.Here, we propose estimators of the concordance based on cross-validation. These esti mators can reduce the optimistic bias originating from plugging in the estimators of the censoring distribution and the parameters of a model. In addition, we present numer ical experiments to illustrate the properties of the proposed estimators in comparison to existing estimators.