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Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data
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作者 LIU Wei LI Yingqiu 《应用概率统计》 CSCD 北大核心 2024年第5期783-799,共17页
In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all usef... In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated. 展开更多
关键词 high-dimensional survival data censored composite conditional quantile coefficient sure screening property rank consistency property
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Modelling the Survival of Western Honey Bee Apis mellifera and the African Stingless Bee Meliponula ferruginea Using Semiparametric Marginal Proportional Hazards Mixture Cure Model
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作者 Patience Isiaho Daisy Salifu +1 位作者 Samuel Mwalili Henri E. Z. Tonnang 《Journal of Data Analysis and Information Processing》 2024年第1期24-39,共16页
Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent s... Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data. 展开更多
关键词 Mixture Cure Models Clustered survival data Correlation Structure Cox-Snell Residuals EM Algorithm Expectation-Solution Algorithm
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Conditional-quantile screening for ultrahigh-dimensional survival data via martingale difference correlation 被引量:2
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作者 Kai Xu Xudong Huang 《Science China Mathematics》 SCIE CSCD 2018年第10期1907-1922,共16页
Using the so-called martingale difference correlation(MDC), we propose a novel censoredconditional-quantile screening approach for ultrahigh-dimensional survival data with heterogeneity(which is often present in such ... Using the so-called martingale difference correlation(MDC), we propose a novel censoredconditional-quantile screening approach for ultrahigh-dimensional survival data with heterogeneity(which is often present in such data). By incorporating a weighting scheme, this method is a natural extension of MDCbased conditional quantile screening, as considered by Shao and Zhang(2014), to handle ultrahigh-dimensional survival data. The proposed screening procedure has a sure-screening property under certain technical conditions and an excellent capability of detecting the nonlinear relationship between independent and censored dependent variables. Both simulation results and an analysis of real data demonstrate the effectiveness of the new censored conditional quantile-screening procedure. 展开更多
关键词 ultrahigh-dimensional survival data martingale difference correlation censored-conditional-quantile screening sure-screening property
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Feature Screening for High-Dimensional Survival Data via Censored Quantile Correlation 被引量:1
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作者 XU Kai HUANG Xudong 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2021年第3期1207-1224,共18页
This paper proposes a new sure independence screening procedure for high-dimensional survival data based on censored quantile correlation(CQC).This framework has two distinctive features:1)Via incorporating a weightin... This paper proposes a new sure independence screening procedure for high-dimensional survival data based on censored quantile correlation(CQC).This framework has two distinctive features:1)Via incorporating a weighting scheme,our metric is a natural extension of quantile correlation(QC),considered by Li(2015),to handle high-dimensional survival data;2)The proposed method not only is robust against outliers,but also can discover the nonlinear relationship between independent variables and censored dependent variable.Additionally,the proposed method enjoys the sure screening property under certain technical conditions.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors. 展开更多
关键词 Censored quantile correlation feature screening high-dimensional survival data rank consistency property sure screening property
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Regression Analysis for the Additive Hazards Model with General Biased Survival Data
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作者 Xiao-lin CHEN 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2020年第3期545-556,共12页
In survival analysis,data are frequently collected by some complex sampling schemes,e.g.,length biased sampling,case-cohort sampling and so on.In this paper,we consider the additive hazards model for the general biase... In survival analysis,data are frequently collected by some complex sampling schemes,e.g.,length biased sampling,case-cohort sampling and so on.In this paper,we consider the additive hazards model for the general biased survival data.A simple and unified estimating equation method is developed to estimate the regression parameters and baseline hazard function.The asymptotic properties of the resulting estimators are also derived.Furthermore,to check the adequacy of the fitted model with general biased survival data,we present a test statistic based on the cumulative sum of the martingale-type residuals.Simulation studies are conducted to evaluate the performance of proposed methods,and applications to the shrub and Welsh Nickel Refiners datasets are given to illustrate the methodology. 展开更多
关键词 additive hazards model estimating equation general biased sampling model checking survival data
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Survivals after liver transplantation for hepatocellular carcinoma:Granular data for a better allocation process?
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作者 Quirino Lai Massimo Rossi 《Hepatobiliary & Pancreatic Diseases International》 SCIE CAS CSCD 2018年第4期374-375,共2页
To the Editor:A large international study has been recently published focusing on the combination of morphological aspects and alpha-fetoprotein(AFP)as predictors of survival in patients with hepatocellular cancer(HCC... To the Editor:A large international study has been recently published focusing on the combination of morphological aspects and alpha-fetoprotein(AFP)as predictors of survival in patients with hepatocellular cancer(HCC)treated with liver transplantation(LT)[1].As a matter of fact,morphology and biology represent the two sides of the same 展开更多
关键词 AFP HCC survivals after liver transplantation for hepatocellular carcinoma:Granular data for a better allocation process
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Joint modeling of longitudinal proportional measurements and survival time with a cure fraction
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作者 SONG Hui PENG YingWei TU DongSheng 《Science China Mathematics》 SCIE CSCD 2016年第12期2427-2442,共16页
In cancer clinical trials and other medical studies, both longitudinal measurements and data on a time to an event(survival time) are often collected from the same patients. Joint analyses of these data would improve ... In cancer clinical trials and other medical studies, both longitudinal measurements and data on a time to an event(survival time) are often collected from the same patients. Joint analyses of these data would improve the efficiency of the statistical inferences. We propose a new joint model for the longitudinal proportional measurements which are restricted in a finite interval and survival times with a potential cure fraction. A penalized joint likelihood is derived based on the Laplace approximation and a semiparametric procedure based on this likelihood is developed to estimate the parameters in the joint model. A simulation study is performed to evaluate the statistical properties of the proposed procedures. The proposed model is applied to data from a clinical trial on early breast cancer. 展开更多
关键词 cure fraction joint model Laplace approximation proportional data simplex distribution survival times
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A Class of Weighted Estimators for Additive Hazards Model in Case-cohort Studies 被引量:3
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作者 Cai-lin DONG Jie ZHOU Liu-quan SUN 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2014年第4期1153-1168,共16页
Case-cohort sampling is a commonly used and efficient method for studying large cohorts. In many situations, some covariates are easily measured on all cohort subjects, and surrogate measurements of the expensive cova... Case-cohort sampling is a commonly used and efficient method for studying large cohorts. In many situations, some covariates are easily measured on all cohort subjects, and surrogate measurements of the expensive covariates also may be observed. In this paper, to make full use of the covariate data collected outside the case-cohort sample, we propose'a class of weighted estimators with general time-varying weights for the additive hazards model, and the estimators are shown to be consistent and asymptotically normal. We also identify the estimator within this class that maximizes efficiency, and simulation studies show that the efficiency gains of the proposed estimator over the existing ones can be substantial in practical situations. A real example is provided. 展开更多
关键词 additive hazards case-cohort study stratified sampling survival data two-phase design
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Case-cohort Analysis with General Additive-multiplicative Hazard Models 被引量:1
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作者 Yi SUN Wen YU Ming ZHENG 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2016年第4期851-866,共16页
The case-cohort design is widely used in large epidemiological studms and prevention trials for cost reduction. In such a design, covariates are assembled only for a subcohort which is a random subset of the entire co... The case-cohort design is widely used in large epidemiological studms and prevention trials for cost reduction. In such a design, covariates are assembled only for a subcohort which is a random subset of the entire cohort and any additional cases outside the subcohort. In this paper, we discuss the case-cohort analysis with a class of general additive-multiplicative hazard models which includes the commonly used Cox model and additive hazard model as special cases. Two sampling schemes for the subcohort, Bernoulli sampling with arbitrary selection probabilities and stratified simple random sampling with fixed subcohort sizes, are discussed. In each setting, an estimating function is constructed to estimate the regression parameters. The resulting estimator is shown to be consistent and asymptotically normally distributed. The limiting variance-covariance matrix can be consistently estimated by the case-cohort data. A simulation study is conducted to assess the finite sample performances of the proposed method and a real example is provided. 展开更多
关键词 additive-multipticative hazard case-cohort design counting process pseudo-score survival data
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Adjusted Log-rank Test with Double Inverse Weighting under Dependent Censoring 被引量:1
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作者 Yi Min GUO Jie ZHOU Liu Quan SUN 《Acta Mathematica Sinica,English Series》 SCIE CSCD 2021年第10期1573-1585,共13页
It is a common issue to compare treatment-specific survival and the weighted log-rank test is the most popular method for group comparison. However, in observational studies, treatments and censoring times are usually... It is a common issue to compare treatment-specific survival and the weighted log-rank test is the most popular method for group comparison. However, in observational studies, treatments and censoring times are usually not independent, which invalidates the weighted log-rank tests. In this paper, we propose adjusted weighted log-rank tests in the presence of non-random treatment assignment and dependent censoring. A double-inverse weighted technique is developed to adjust the weighted log-rank tests. Specifically, inverse probabilities of treatment and censoring weighting are involved to balance the baseline treatment assignment and to overcome dependent censoring, respectively. We derive the asymptotic distribution of the proposed adjusted tests under the null hypothesis, and propose a method to obtain the critical values. Simulation studies show that the adjusted log-rank tests have correct sizes whereas the traditional weighted log-rank tests may fail in the presence of non-random treatment assignment and dependent censoring. An application to oropharyngeal carcinoma data from the Radiation Therapy Oncology Group is provided for illustration. 展开更多
关键词 Adjusted log-rank test dependent censoring double inverse weighting survival data
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Jackknifed random weighting for Cox proportional hazards model
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作者 LI Xiao 1 ,WU YaoHua 2,& TU DongSheng 1 1 Cancer Research Institute,Queen’s University,Kingston,Ontario K 7L 3N6,Canada 2 Department of Finance and Statistics,University of Science and Technology of China,Hefei 230026,China 《Science China Mathematics》 SCIE 2012年第4期775-786,共12页
The Cox proportional hazards model is the most used statistical model in the analysis of survival time data.Recently,a random weighting method was proposed to approximate the distribution of the maximum partial likeli... The Cox proportional hazards model is the most used statistical model in the analysis of survival time data.Recently,a random weighting method was proposed to approximate the distribution of the maximum partial likelihood estimate for the regression coefficient in the Cox model.This method was shown not as sensitive to heavy censoring as the bootstrap method in simulation studies but it may not be second-order accurate as was shown for the bootstrap approximation.In this paper,we propose an alternative random weighting method based on one-step linear jackknife pseudo values and prove the second accuracy of the proposed method.Monte Carlo simulations are also performed to evaluate the proposed method for fixed sample sizes. 展开更多
关键词 Cox proportional hazards model JACKKNIFE random weighting second-order accuracy simulations survival data
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A simple construction of optimal estimation in multivariate marginal Cox regression
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作者 CUI WenQuan YING ZhiLiang ZHAO LinCheng 《Science China Mathematics》 SCIE 2012年第9期1827-1857,共31页
The multivariate extension of the Cox model proposed by Wei,Lin and Weissfeld in 1989 has been widely used for analyzing multivariate survival data.Under the model assumption,failure times from an individual are assum... The multivariate extension of the Cox model proposed by Wei,Lin and Weissfeld in 1989 has been widely used for analyzing multivariate survival data.Under the model assumption,failure times from an individual are assumed to marginally follow their respective proportional hazards regression relation,leaving the joint distribution completely unspecified.This paper presents a simple approach to efficiency improvement through segmentation of stochastic integrals in the marginal estimating equations and incorporation of the limiting covariance structure.It is shown that when partition of the time interval is done at a suitable rate,the resulting estimator is consistent and asymptotically normal.Through the reproducing kernel Hilbert space arising from the covariance function of the limiting Gaussian process,it is also shown that the proposed estimator is asymptotically optimal within a reasonable class of estimators under marginal specification.Simulations are conducted to assess the finite-sample performance of the proposed method. 展开更多
关键词 multivariate survival data marginal proportional hazards regression WLW method estimatingequations empirical processes MARTINGALE reproducing kernel Hilbert spaces limit theorem of reproducingkernels
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Estimating Cumulative Treatment Effect Under an Additive Hazards Model
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作者 LU Xiaoliang ZHANG Baoxue SUN Liuquan 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2021年第2期724-734,共11页
In clinical and epidemiologic studies of time to event,the treatment effect is often of direct interest,and the treatment effect is not constant over time.In this paper,the authors propose an estimator for the cumulat... In clinical and epidemiologic studies of time to event,the treatment effect is often of direct interest,and the treatment effect is not constant over time.In this paper,the authors propose an estimator for the cumulative hazard difference under a stratified additive hazards model.The asymptotic properties of the resulting estimator are established,and the finite-sample properties are examined through simulation studies.An application to a liver cirrhosis data set from the Copenhagen Study Group for Liver Diseases is provided. 展开更多
关键词 Additive hazards model cumulative hazards survival data time-dependent effect
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