In this chapter, we first develop semi-parametric regression models for covariate specific predictive values of biomarkers, which have conditional interpretations. We then propose a marginalized covariate-adjusted predictive accuracy to represent a summary predictive measure of a biomarker in the whole ...
We achieve the first goal through estimating a covariate-specific treatment effect (CSTE) curve modeled as an unknown function of a weighted linear combination of all baseline covariates. The weight or the coefficient for each covariate is estimated by fitting a sparse semi-parametric logistic single...
Flexible hazard ratio curves taking a specific covariate value as referenceArtur AraújoLuís MeiraMachado
covariate‐specificdiscriminationheterogeneitytime‐dependent ROC curveSeveral studies for the clinical validity of circulating tumor cells (CTCs) in metastatic breast cancer were conducted showing that it is a prognostic biomarker of overall survival. In this work, we consider an individual patient data ...
To account for the covariate effect, we propose semiparametric models for covariate specific ROC curves corresponding to the two time-dependent ROC curve definitions, respectively. We show that the estimators are consistent and converge to Gaussian processes. In the case of no covariates, the ...
In particular, we develop estimation and inference procedures for covariate-specific quantiles of the residual time under the Cox model. Our methods and theory are assessed by simulations, and demonstrated in analysis of two real data sets.
Summary Covariate-specific receiver operating characteristic (ROC) curves are often used to evaluate the classification accuracy of a medical diagnostic te... D Liu,Xiao〩ua Zhou - 《Biometrics》 被引量: 30发表: 2011年 ROC curve and covariates: extending induced methodology to the non-parametric ...
This method enables us to investigate covariate-specific treatment effects and make personalised treatment selection in a flexible fashion. We develop a method that combines local linear regression and penalised quasi-likelihood to estimate the weight for each covariate, the unknown treatment effect curve...
We achieve the first goal through estimating a covariate-specific treatment effect (CSTE) curve modeled as an unknown function of a weighted linear combination of all baseline covariates. The weight or the coefficient for each covariate is estimated by fitting a sparse semi-parametric logistic single...
We herein develop a framework for estimating covariate-specific ROC curves that integrates robustness, heteroscedasticity, and stochastic ordering. The latter is of specific relevance in the given application since biometric recognition systems are typically calibrated to assign higher scores to matching ...