Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring
The paper investigates efficiently learning Cox regression models when data is outcome-dependent truncated and right-censored. It proves that if the baseline hazard is known, first-order optimization works despite non-convexity. However, joint estimation of the regression coefficient and baseline can lead to suboptimal local minima.