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 ...
Study Design and Setting Bayesian nonparametric covariate-specific ROC curves were constructed to examine the performance/positivity thresholds in covariate subgroups. Standard ROC curves were constructed. Three scenarios were outlined based on comparison between subgroups and standard ROC curve conclusion: (...
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 ...
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 ...
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.
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...
Pepe (2010a): "Semiparametric methods for evaluating the covariate-specific predictiveness of continuous markers in matched case-control studies," Journal of the Royal Statistical Society, Series B, 59, 437-456.Huang Y, Pepe M. Semiparametric methods for evaluating the covariate-specific predictive...
The asymptotic normality is established, both for the regression parameter estimator and the estimator for the covariate-specific ROC curve at a fixed false positive point. Simulation results show that the Wilcoxon estimator compares favorably to its main competitors in terms of the standard error, ...
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 ...