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stat.ME2026
When are time-to-event models a waste of time? Bridging mixture cure models and positive-unlabeled learning for binary classification under right-censoring
Qin Weng, Matthew M. Engelhard
In clinical settings, predicting binary outcomes is often complicated by right-censoring, which prevents us from distinguishing individuals in whom the event never occurs (i.e., ne…
stat.ME2026
Double Variable Importance Matching to Estimate Distinct Causal Effects on Event Probability and Timing
Yuqi Li, Quinn Lanners, Matthew M. Engelhard
In many clinical contexts, estimating effects of treatment in time-to-event data is complicated not only by confounding, censoring, and heterogeneity, but also by the presence of a…