第14回統計科学セミナー（2024年10月11日開催）「Joint AFT random-effect modeling approach for clustered competing-risks data によるオンライン開催」のご案内

講演概要：

　Competing risks data arise when occurrence of an event hinders observation of other types of events, and they are encountered in various research areas including biomedical research. These data have been usually analyzed using the hazard-based models, not survival times themselves.

In this talk, we propose a joint accelerated failure time (AFT) modeling approach to model clustered competing risks data. Times to competing events are assumed to be log-linear with normal errors and correlated through a scaled random effect that follows a zero-mean normal distribution. Inference on the model parameters is based on the h-likelihood. Performance of the proposed method is evaluated through extensive simulation studies. The simulation results show that the
estimated regression parameters are robust against the violation of the assumed parametric distributions. The proposed method is illustrated with two real competing risks data sets. In addition, we present further extensions.
Keywords: Joint AFT model; competing risks; h-likelihood; clustered survival data