EcoSta 2021: Start Registration
View Submission - EcoSta2021
A0638
Title: Semiparametric methods for left-truncated and right-censored survival data with covariate measurement error Authors:  Grace Yi - University of Western Ontario (Canada) [presenting]
Abstract: Biased samples caused by left-truncation (or length-biased sampling) and measurement error often accompany survival analysis. While such data frequently arise in practice, little work has been available to address these features simultaneously. We explore valid inference methods for handling left-truncated and right-censored survival data with measurement error under the widely used Cox model. We first exploit a flexible estimator for the survival model parameters, which does not require the baseline hazard function specification. To improve the efficiency, we further develop an augmented nonparametric maximum likelihood estimator. We establish asymptotic results and examine the efficiency and robustness issues for the proposed estimators. The proposed methods enjoy appealing features that the distributions of the covariates and truncation times are left unspecified. Numerical studies are reported to assess the finite sample performance of the proposed methods.