The conditional maximum likelihood estimation for the Cox-Aalen model with doubly truncated data - Su, Chun-Lung; Shen, Pao-sheng (Statistics (Berlin, DDR), 2025)
Doubly truncated (DT) data occur when event times are observed only if they fall within subject-specific, possibly random, intervals. In this article, we consider conditional maximum likelihood estimation for the regression parameters of the Cox-Aalen model with DT data. Based on gradient projection method (GPM), we propose computational algorithms for obtaining the conditional maximum likelihood estimator (cMLE). The proposed cMLE is shown to be consistent and asymptotically normal. Simulation studies show that the cMLE performs well in finite samples.