Variable selection and estimation for the additive hazards model subject to left-truncation, right-censoring and measurement error in covariates
Li‐Pang Chen
Abstract
Variable selection with censored survival data is of great practical importance, and several methods have been proposed for variable selection based on different models. However, the impacts of biased samples caused by left-truncation and covariate measurement error to variable selection are not fully explored. In this paper, we mainly focus on the additive hazards model and analyze variable selection and estimation for survival data subject to left-truncation and measurement error in covariates. We develop the three-stage procedure to correct for error effects, select informative variables, and estimate the parameters of interest simultaneously. Numerical studies are reported to assess the performance of the proposed methods.
Topics & Concepts
CovariateCensoring (clinical trials)StatisticsMathematicsTruncation (statistics)Feature selectionProportional hazards modelObservational errorSelection (genetic algorithm)Variable (mathematics)EconometricsAccelerated failure time modelEstimationComputer scienceArtificial intelligenceManagementMathematical analysisEconomicsStatistical Methods and InferenceStatistical Methods and Bayesian InferenceAdvanced Causal Inference Techniques