Conference Agenda

The pseudo-observation regression approach provides a flexible alternative to the omnipresent proportional hazards model when modeling time-to-event outcomes. In this approach, estimands representable as expectations are fitted to regression models using covariates of interest. Exemplary estimands that fit this framework are the restricted mean time lost (in competing risks models) or the survival function at a fixed time-point (in simple survival models).
Even though consistent parameter estimates are readily obtained using standard statistical software, variance estimation turns out to be a more intricate task: We verify the longstanding conjecture that the usual Huber-White estimator is not consistent. By confirming that a plug-in estimator can be used instead, we obtain asymptotically exact and consistent tests for general linear hypotheses in the parameters of the model. Additionally, we confirm that naive bootstrapping can not be used for covariance estimation in the pseudo-observation approach either. However, it can still be used for hypothesis testing by applying a suitable studentization. These methods are evaluated in an extensive simulation study and exemplified with a real data analysis.