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Estimating Cumulative Treatment Effect Under an Additive Hazards Model

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Journal of Systems Science & Complexity 2021年第2期34卷 724-734页

作者： LU Xiaoliang ZHANG Baoxue SUN Liuquan School of Statistics Capital University of Economics and BusinessBeijing 100070China Institute of Applied Mathematics Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

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 *** this paper,the authors propose an estimator for the cumulative hazard difference under a stratified additive hazards *** asymptotic properties of the resulting estimator are established,and the finite-sample properties are examined through simulation *** application to a liver cirrhosis data set from the Copenhagen Study Group for Liver Diseases is provided.

关键词： Additive hazards model cumulative hazards survival data time-dependent effect

A Class of Weighted Estimators for Additive Hazards Model in Case-cohort Studies

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Acta Mathematicae Applicatae Sinica 2014年第4期30卷 1153-1168页

作者： Cai-lin DONG Jie ZHOU Liu-quan SUN School of Mathematics and Statistics Huazhong Normal University Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics Capital Normal University

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.

关键词： additive hazards case-cohort study stratified sampling survival data two-phase design

Adjusted Log-rank Test with Double Inverse Weighting under Dependent Censoring

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Acta Mathematica Sinica,English Series 2021年第10期37卷 1573-1585页

作者： Yi Min GUO Jie ZHOU Liu Quan SUN Institute of Applied Mathematics Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190P.R.China University of Chinese Academy of Sciences Beijing 100049P.R.China School of Mathematics of Sciences Capital Normal UniversityBeijing 100048P.R.China

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.

关键词： Adjusted log-rank test dependent censoring double inverse weighting survival data

Case-cohort Analysis with General Additive-multiplicative Hazard Models

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Acta Mathematicae Applicatae Sinica 2016年第4期32卷 851-866页

作者： Yi SUN Wen YU Ming ZHENG Department of Statistics School of ManagementFudan University

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.

关键词： additive-multipticative hazard case-cohort design counting process pseudo-score survival data

Jackknifed random weighting for Cox proportional hazards model

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Science China Mathematics 2012年第4期55卷 775-786页

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 Cancer Research Institute Queen’s University Kingston Canada Department of Finance and Statistics University of Science and Technology of China Hefei China

The Cox proportional hazards model is the most used statistical model in the analysis of survival time ***,a random weighting method was proposed to approximate the distribution of the maximum partial likelihood estimate for the regression coefficient in the Cox *** 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 *** 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 *** Carlo simulations are also performed to evaluate the proposed method for fixed sample sizes.

关键词： Cox proportional hazards model jackknife random weighting second-order accuracy simulations survival data