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Jian Huang - One of the best experts on this subject based on the ideXlab platform.
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Subgroup analysis in the heterogeneous Cox Model.
Statistics in medicine, 2020Co-Authors: Jian Huang, Li Liu, Defeng Sun, Xingqiu ZhaoAbstract:In the analysis of censored survival data, to avoid a biased inference of treatment effects on the hazard function of the survival time, it is important to consider the treatment heterogeneity. Without requiring any prior knowledge about the subgroup structure, we propose a data driven subgroup analysis procedure for the heterogeneous Cox Model by constructing a pairwise fusion penalized partial likelihood-based objective function. The proposed method can determine the number of subgroups, identify the group structure, and estimate the treatment effect simultaneously and automatically. A majorized alternating direction method of multipliers algorithm is then developed to deal with the numerically challenging high-dimensional problems. We also establish the oracle properties and the Model selection consistency for the proposed penalized estimator. Our proposed method is evaluated by simulation studies and further illustrated by the analysis of the breast cancer data.
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Estimating high‐dimensional additive Cox Model with time‐dependent covariate processes
Scandinavian Journal of Statistics, 2018Co-Authors: Jiakun Jiang, Fanyin Zhou, Jian Huang, Huazhen LinAbstract:This paper is concerned with the estimation in the additive Cox Model with time‐dependent covariates when the number of additive components p is greater than the sample size n. By combining spline representation and the group lasso penalty, a penalized partial likelihood approach to estimating the unknown component functions is proposed. Given the non‐iid nature of the log partial likelihood function and the nonparametric complexities of the component function estimation, it is challenging to analyze the theoretical properties of the proposed estimation. Through concentration inequities developed for martingale differences in the context of the additive Cox Model, we establish nonasymptotic oracle inequalities for the group lasso in the additive Cox Model with p=eo(n) under the compatibility and cone invertibility factors conditions on the Hessian matrix. An interesting and surprising aspect of our result is that the complexity of the component functions affects not only the approximation error but also the stochastic error. This is quite different from the additive mean Models and suggests that the additive Cox Model is more difficult to estimate than the additive mean Models in high‐dimensional settings.
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a spline based semiparametric maximum likelihood estimation method for the Cox Model with interval censored data
Scandinavian Journal of Statistics, 2010Co-Authors: Yingying Zhang, Lei Hua, Jian HuangAbstract:Abstract. We propose a spline-based semiparametric maximum likelihood approach to analysing the Cox Model with interval-censored data. With this approach, the baseline cumulative hazard function is approximated by a monotone B-spline function. We extend the generalized Rosen algorithm to compute the maximum likelihood estimate. We show that the estimator of the regression parameter is asymptotically normal and semiparametrically efficient, although the estimator of the baseline cumulative hazard function converges at a rate slower than root-n. We also develop an easy-to-implement method for consistently estimating the standard error of the estimated regression parameter, which facilitates the proposed inference procedure for the Cox Model with interval-censored data. The proposed method is evaluated by simulation studies regarding its finite sample performance and is illustrated using data from a breast cosmesis study.
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a spline based semiparametric maximum likelihood estimation method for the Cox Model with interval censored data
Scandinavian Journal of Statistics, 2010Co-Authors: Yingying Zhang, Jian HuangAbstract:We propose a spline-based semiparametric maximum likelihood approach to analysing the Cox Model with interval-censored data. With this approach, the baseline cumulative hazard function is approximated by a monotone B-spline function. We extend the generalized Rosen algorithm to compute the maximum likelihood estimate. We show that the estimator of the regression parameter is asymptotically normal and semiparametrically efficient, although the estimator of the baseline cumulative hazard function converges at a rate slower than root-"n". We also develop an easy-to-implement method for consistently estimating the standard error of the estimated regression parameter, which facilitates the proposed inference procedure for the Cox Model with interval-censored data. The proposed method is evaluated by simulation studies regarding its finite sample performance and is illustrated using data from a breast cosmesis study. Copyright (c) 2010 Board of the Foundation of the Scandinavian Journal of Statistics.
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Efficient estimation of the partly linear additive Cox Model
The Annals of Statistics, 1999Co-Authors: Jian HuangAbstract:The partly linear additive Cox Model is an extension of the (linear) Cox Model and allows flexible Modeling of covariate effects semiparametrically. We study asymptotic properties of the maximum partial likelihood estimator of this Model with right-censored data using polynomial splines. We show that, with a range of choices of the smoothing parameter (the number of spline basis functions) required for estimation of the nonparametric components, the estimator of the finite-dimensional regression parameter is root-n consistent, asymptotically normal and achieves the semiparametric information bound. Rates of convergence for the estimators of the nonparametric components are obtained. They are comparable to the rates in nonparametric regression. Implementation of the estimation approach can be done easily and is illustrated by using a simulated example.
Yingying Zhang - One of the best experts on this subject based on the ideXlab platform.
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a spline based semiparametric maximum likelihood estimation method for the Cox Model with interval censored data
Scandinavian Journal of Statistics, 2010Co-Authors: Yingying Zhang, Lei Hua, Jian HuangAbstract:Abstract. We propose a spline-based semiparametric maximum likelihood approach to analysing the Cox Model with interval-censored data. With this approach, the baseline cumulative hazard function is approximated by a monotone B-spline function. We extend the generalized Rosen algorithm to compute the maximum likelihood estimate. We show that the estimator of the regression parameter is asymptotically normal and semiparametrically efficient, although the estimator of the baseline cumulative hazard function converges at a rate slower than root-n. We also develop an easy-to-implement method for consistently estimating the standard error of the estimated regression parameter, which facilitates the proposed inference procedure for the Cox Model with interval-censored data. The proposed method is evaluated by simulation studies regarding its finite sample performance and is illustrated using data from a breast cosmesis study.
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a spline based semiparametric maximum likelihood estimation method for the Cox Model with interval censored data
Scandinavian Journal of Statistics, 2010Co-Authors: Yingying Zhang, Jian HuangAbstract:We propose a spline-based semiparametric maximum likelihood approach to analysing the Cox Model with interval-censored data. With this approach, the baseline cumulative hazard function is approximated by a monotone B-spline function. We extend the generalized Rosen algorithm to compute the maximum likelihood estimate. We show that the estimator of the regression parameter is asymptotically normal and semiparametrically efficient, although the estimator of the baseline cumulative hazard function converges at a rate slower than root-"n". We also develop an easy-to-implement method for consistently estimating the standard error of the estimated regression parameter, which facilitates the proposed inference procedure for the Cox Model with interval-censored data. The proposed method is evaluated by simulation studies regarding its finite sample performance and is illustrated using data from a breast cosmesis study. Copyright (c) 2010 Board of the Foundation of the Scandinavian Journal of Statistics.
Xingqiu Zhao - One of the best experts on this subject based on the ideXlab platform.
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Subgroup analysis in the heterogeneous Cox Model.
Statistics in medicine, 2020Co-Authors: Jian Huang, Li Liu, Defeng Sun, Xingqiu ZhaoAbstract:In the analysis of censored survival data, to avoid a biased inference of treatment effects on the hazard function of the survival time, it is important to consider the treatment heterogeneity. Without requiring any prior knowledge about the subgroup structure, we propose a data driven subgroup analysis procedure for the heterogeneous Cox Model by constructing a pairwise fusion penalized partial likelihood-based objective function. The proposed method can determine the number of subgroups, identify the group structure, and estimate the treatment effect simultaneously and automatically. A majorized alternating direction method of multipliers algorithm is then developed to deal with the numerically challenging high-dimensional problems. We also establish the oracle properties and the Model selection consistency for the proposed penalized estimator. Our proposed method is evaluated by simulation studies and further illustrated by the analysis of the breast cancer data.
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Semiparametric Inference for the Functional Cox Model
Journal of the American Statistical Association, 2020Co-Authors: Meiling Hao, Kin-yat Liu, Xingqiu ZhaoAbstract:This article studies penalized semiparametric maximum partial likelihood estimation and hypothesis testing for the functional Cox Model in analyzing right-censored data with both functional and sca...
Robert Gray - One of the best experts on this subject based on the ideXlab platform.
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Modeling Survival Data: Extending the Cox Model
Journal of the American Statistical Association, 2002Co-Authors: Robert GrayAbstract:(2002). Modeling Survival Data: Extending the Cox Model. Journal of the American Statistical Association: Vol. 97, No. 457, pp. 353-354.
Lei Hua - One of the best experts on this subject based on the ideXlab platform.
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a spline based semiparametric maximum likelihood estimation method for the Cox Model with interval censored data
Scandinavian Journal of Statistics, 2010Co-Authors: Yingying Zhang, Lei Hua, Jian HuangAbstract:Abstract. We propose a spline-based semiparametric maximum likelihood approach to analysing the Cox Model with interval-censored data. With this approach, the baseline cumulative hazard function is approximated by a monotone B-spline function. We extend the generalized Rosen algorithm to compute the maximum likelihood estimate. We show that the estimator of the regression parameter is asymptotically normal and semiparametrically efficient, although the estimator of the baseline cumulative hazard function converges at a rate slower than root-n. We also develop an easy-to-implement method for consistently estimating the standard error of the estimated regression parameter, which facilitates the proposed inference procedure for the Cox Model with interval-censored data. The proposed method is evaluated by simulation studies regarding its finite sample performance and is illustrated using data from a breast cosmesis study.
