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
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
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
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
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
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
BACKGROUND Chronic hepatitis B often progresses silently toward hepatocellular carcinoma(HCC),a leading cause of mortality worldwide.Early detection of HCC is crucial,yet challenging.AIM To investigate the role of dyn...BACKGROUND Chronic hepatitis B often progresses silently toward hepatocellular carcinoma(HCC),a leading cause of mortality worldwide.Early detection of HCC is crucial,yet challenging.AIM To investigate the role of dynamic changes in alkaline phosphatase to prealbumin ratio(APR)in hepatitis B progression to HCC.METHODS Data from 4843 patients with hepatitis B(January 2015 to January 2024)were analyzed.HCC incidence rates in males and females were compared using the log-rank test.Data were evaluated using Kaplan–Meier analysis.The Linear Mixed-Effects Model was applied to track the fluctuation of APR levels over time.Furthermore,Joint Modeling of Longitudinal and Survival data was employed to investigate the temporal relationship between APR and HCC risk.RESULTS The incidence of HCC was higher in males.To ensure the model’s normality assumption,this study applied a logarithmic transformation to APR,yielding ratio.Ratio levels were higher in females(t=5.26,P<0.01).A 1-unit increase in ratio correlated with a 2.005-fold higher risk of HCC in males(95%CI:1.653-2.431)and a 2.273-fold higher risk in females(95%CI:1.620-3.190).CONCLUSION Males are more prone to HCC,while females have higher APR levels.Despite no baseline APR link,rising APR indicates a higher HCC risk.展开更多
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
Tumor metabolism often interferes with the immune microenvironment.Although natural killer(NK)cells play pivotal roles in antitumor immunity,the connection between NK cells and tumor metabolism remains unclear.Our sys...Tumor metabolism often interferes with the immune microenvironment.Although natural killer(NK)cells play pivotal roles in antitumor immunity,the connection between NK cells and tumor metabolism remains unclear.Our systematic analysis of multiomics data and survival data from colorectal cancer(CRC)patients uncovered a novel association between mitochondrial acetyl-CoA acetyltransferase 1(ACAT1)and NK cell infiltration that influences disease progression.ACAT1,a metabolic enzyme involved in reversible conversion of acetoacetyl-CoA to two molecules of acetyl-CoA,exhibits nuclear protein acetylation activity through its translocation.Under immune stimulation,mitochondrial ACAT1 can be phosphorylated at serine 60(S60)and enters the nucleus;however,this process is hindered in nutrient-poor tumor microenvironments.Nuclear ACAT1 directly acetylates lysine 146 of p50(NFKB1),attenuating its DNA binding and transcriptional repression activity and thereby increasing the expression of immune-related factors,which in turn promotes NK cell recruitment and activation to suppress colorectal cancer growth.Furthermore,significant associations are found among low nuclear ACAT1 levels,decreased S60 phosphorylation,and reduced NK cell infiltration,as well as poor prognosis in CRC.Our findings reveal an unexpected function of ACAT1 as a nuclear acetyltransferase and elucidate its role in NK cell-dependent antitumor immunity through p50 acetylation.展开更多
Simultaneously finding active predictors and controlling the false discovery rate(FDR)for high-dimensional survival data is an important but challenging statistical problem.In this paper,the authors propose a novel va...Simultaneously finding active predictors and controlling the false discovery rate(FDR)for high-dimensional survival data is an important but challenging statistical problem.In this paper,the authors propose a novel variable selection procedure with error rate control for the high-dimensional Cox model.By adopting a data-splitting strategy,the authors construct a series of symmetric statistics and then utilize the symmetry property to derive a data-driven threshold to achieve error rate control.The authors establish finite-sample and asymptotic FDR control results under some mild conditions.Simulation results as well as a real data application show that the proposed approach successfully controls FDR and is often more powerful than the competing approaches.展开更多
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
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
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
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
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
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
基金Outstanding Youth Foundation of Hunan Provincial Department of Education(Grant No.22B0911)。
文摘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.
基金supported by the National Statistical Scientific Research Projects(Grant No.2015LZ54)
文摘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.
基金supported by the National Natural Science Foundation of China under Grant No.11901006the Natural Science Foundation of Anhui Province under Grant Nos.1908085QA06 and 1908085MA20。
文摘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.
文摘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.
文摘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
文摘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.
文摘BACKGROUND Chronic hepatitis B often progresses silently toward hepatocellular carcinoma(HCC),a leading cause of mortality worldwide.Early detection of HCC is crucial,yet challenging.AIM To investigate the role of dynamic changes in alkaline phosphatase to prealbumin ratio(APR)in hepatitis B progression to HCC.METHODS Data from 4843 patients with hepatitis B(January 2015 to January 2024)were analyzed.HCC incidence rates in males and females were compared using the log-rank test.Data were evaluated using Kaplan–Meier analysis.The Linear Mixed-Effects Model was applied to track the fluctuation of APR levels over time.Furthermore,Joint Modeling of Longitudinal and Survival data was employed to investigate the temporal relationship between APR and HCC risk.RESULTS The incidence of HCC was higher in males.To ensure the model’s normality assumption,this study applied a logarithmic transformation to APR,yielding ratio.Ratio levels were higher in females(t=5.26,P<0.01).A 1-unit increase in ratio correlated with a 2.005-fold higher risk of HCC in males(95%CI:1.653-2.431)and a 2.273-fold higher risk in females(95%CI:1.620-3.190).CONCLUSION Males are more prone to HCC,while females have higher APR levels.Despite no baseline APR link,rising APR indicates a higher HCC risk.
基金supported by the Fundamental Research Funds for the Central Universities of ChinaNational Natural Science Foundation of China (Grant No. 11601060)+1 种基金Dalian High Level Talent Innovation Programme (Grant No.2015R051)Research Grants from Natural Sciences and Engineering Research Council of Canada
文摘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.
基金supported by the National Key R&D Program of China(2023YFC3402100)National Natural Science Foundation of China(82273241,82303224,82372826)+2 种基金Guangdong Basic and Applied Basic Research Foundation(SL2023A04J01630)Sanming Project of Medicine in Shenzhen(SZSM202211017)Cancer Innovative Research Program of Sun Yat-sen University Cancer Center(CIRP-SYSUCC-0004).
文摘Tumor metabolism often interferes with the immune microenvironment.Although natural killer(NK)cells play pivotal roles in antitumor immunity,the connection between NK cells and tumor metabolism remains unclear.Our systematic analysis of multiomics data and survival data from colorectal cancer(CRC)patients uncovered a novel association between mitochondrial acetyl-CoA acetyltransferase 1(ACAT1)and NK cell infiltration that influences disease progression.ACAT1,a metabolic enzyme involved in reversible conversion of acetoacetyl-CoA to two molecules of acetyl-CoA,exhibits nuclear protein acetylation activity through its translocation.Under immune stimulation,mitochondrial ACAT1 can be phosphorylated at serine 60(S60)and enters the nucleus;however,this process is hindered in nutrient-poor tumor microenvironments.Nuclear ACAT1 directly acetylates lysine 146 of p50(NFKB1),attenuating its DNA binding and transcriptional repression activity and thereby increasing the expression of immune-related factors,which in turn promotes NK cell recruitment and activation to suppress colorectal cancer growth.Furthermore,significant associations are found among low nuclear ACAT1 levels,decreased S60 phosphorylation,and reduced NK cell infiltration,as well as poor prognosis in CRC.Our findings reveal an unexpected function of ACAT1 as a nuclear acetyltransferase and elucidate its role in NK cell-dependent antitumor immunity through p50 acetylation.
基金supported by the National Natural Science Foundation of China under Grant Nos.12301364,12322112,12071038,11925106,12231011,11931001,12226007,12326325,and 12131006the National Key R&D Program of China under Grant Nos.2022YFA1003703 and 2022YFA1003800+3 种基金the Natural Science Foundation of Anhui Province under Grant No.2308085QA09the Fundamental Research Funds for the Central Universities under Grant No.2243200006the Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences under Grant No.CI2023C063YLLthe University Grant Council of Hong Kong。
文摘Simultaneously finding active predictors and controlling the false discovery rate(FDR)for high-dimensional survival data is an important but challenging statistical problem.In this paper,the authors propose a novel variable selection procedure with error rate control for the high-dimensional Cox model.By adopting a data-splitting strategy,the authors construct a series of symmetric statistics and then utilize the symmetry property to derive a data-driven threshold to achieve error rate control.The authors establish finite-sample and asymptotic FDR control results under some mild conditions.Simulation results as well as a real data application show that the proposed approach successfully controls FDR and is often more powerful than the competing approaches.
基金partly supported by the National Natural Science Foundation of China Grants(No.11231010,11171330 and 11101314)Key Laboratory of RCSDS,CAS(No.2008DP173182)and BCMIIS
文摘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.
基金supported by the National Natural Science Foundation of China(11101091)the Specialized Research Fund for the Doctoral Program of Higher Education of China(20110071120023)supported by the National Natural Science Foundation of China(10971033)
文摘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.
基金Supported by Beijing Municipal Education Commission (Grant No. KM202010028017)the National Natural Science Foundation of China (Grant Nos. 11771431 and 11690015)+2 种基金the Key Laboratory of RCSDSCAS (Grant No. 2008DP173182)the Academy for Multidisciplinary Studies of Capital Normal University。
文摘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.
基金supported by National Natural Science Foundation of China (Grant Nos.10471136 and 10971210)the Knowledge Innovation Program of Chinese Academy of Sciences (Grant No.KJCX3-SYW-S02)
文摘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.
基金the National Natural Science Foundation of China under Grant Nos.11671268,11771431 and 11690015the Key Laboratory of RCSDS,CAS under Grant No.2008DP173182。
文摘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.
基金supported by Natural Science and Engineering Research Council of Canada and National Natural Science Foundation of China(Grant No.10871188)
文摘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.
