 Improving efficiency using the Rao–Blackwell theorem in corrected and conditional score estimation methods for joint modelsYih‐Huei Huang, Wen‐Han Hwang and Fei‐Yin Chen Biometrics, 2016, vol. 72, issue 4, 1136-1144 Abstract: Longitudinal covariates in survival models are generally analyzed using random effects models. By framing the estimation of these survival models as a functional measurement error problem, semiparametric approaches such as the conditional score or the corrected score can be applied to find consistent estimators for survival model parameters without distributional assumptions on the random effects. However, in order to satisfy the standard assumptions of a survival model, the semiparametric methods in the literature only use covariate data before each event time. This suggests that these methods may make inefficient use of the longitudinal data. We propose an extension of these approaches that follows a generalization of Rao–Blackwell theorem. A Monte Carlo error augmentation procedure is developed to utilize the entirety of longitudinal information available. The efficiency improvement of the proposed semiparametric approach is confirmed theoretically and demonstrated in a simulation study. A real data set is analyzed as an illustration of a practical application. Date: 2016 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:72:y:2016:i:4:p:1136-1144 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Biometrics from The International Biometric Society |
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