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Regression Analysis of Misclassified current status data with Informative Observation Times

Journal of Systems Science & Complexity 2023年第3期36卷 1250-1264页

作者： WANG Wenshan XU Da ZHAO Shishun SUN Jianguo Center for Applied Statistical Research School of MathematicsJilin UniversityChangchun 130012China Key Laboratory of Applied Statistics of MOE and School of Mathematics and Statistics Northeast Normal UniversityChangchun 130024China Department of Statistics University of MissouriColumbiaMO 65211USA

Misclassified current status data arises if each study subject can only be observed once and the observation status is determined by a diagnostic test with imperfect sensitivity and specificity.For the situation,another issue that may occur is that the observation time may be correlated with the interested failure time,which is often referred to as informative censoring or observation times.It is well-known that in the presence of informative censoring,the analysis that ignores it could yield biased or even misleading results.In this paper,the authors consider such data and propose a frailty-based inference procedure.In particular,an EM algorithm based on Poisson latent variables is developed and the asymptotic properties of the resulting estimators are established.The numerical results show that the proposed method works well in practice and an application to a set of real data is provided.

关键词： current status data EM algorithm informative censoring misclassification proportional hazard model

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A New Approach for Regression Analysis of Multivariate current status data with Informative Censoring

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Communications in Mathematics and Statistics 2023年第4期11卷 775-794页

作者： Huiqiong Li Chenchen Ma Jianguo Sun Niansheng Tang Department of Statistics Yunnan UniversityKunming 650091People’s Republic of China Department of Statistics University of MissouriColumbiaMO 65211USA

Regression analysis of interval-censored failure time data has recently attracted a great deal of attention partly due to their increasing occurrences in many fields.In this paper,we discuss a type of such data,multivariate current status data,where in addition to the complex interval data structure,one also faces dependent or informative censoring.For inference,a sieve maximum likelihood estimation procedure is developed and the proposed estimators of regression parameters are shown to be asymptotically consistent and efficient.For the implementation of the method,an EM algorithm is provided,and the results from an extensive simulation study demonstrate the validity and good performance of the proposed inference procedure.For an illustration,the proposed approach is applied to a tumorigenicity experiment.

关键词： Additive hazards model current status data Informative censoring

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Efficient Estimation for Semiparametric Varying-Coefficient Partially Linear Regression Models with current status data

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Acta Mathematicae Applicatae Sinica 2009年第2期25卷 195-204页

作者： Tao Hu Heng-jian Cui Xing-wei Tong School of Mathematical Sciences Beijing Normal University Laboratory of Mathematics and Complex Systems Ministry of Education Beijing 100875 China

This article considers a semiparametric varying-coefficient partially linear regression model with current status data. The semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A Sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estimator for the unknown smooth function is obtained and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies are conducted to examine the small-sample properties of the proposed estimates and a real dataset is used to illustrate our approach.

关键词： Partly linear model varying-coefficient current status data asymptotically efficient estimator sieve MLE

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Pseudo-Maximum Likelihood Estimation in the Hazards Cure Model with a Single Change Point for current status data

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Journal of Mathematical Research with Applications 2020年第3期40卷 320-330页

作者： Bing WANG Xiaoguang WANG Department of Mathematics Dalian University of Technology

The change-point hazards model has received much attention, since it can not only display the impacts of treatments or medical breakthroughs more directly, but also provide the time point when those impacts occur. In this paper, we propose the single change-point hazards model for current status survival data with long-term survivors and investigate the estimation for the proposed model. Large-sample properties of the estimators are established. Simulation studies are carried out to evaluate the finite-sample performance of the estimation.

关键词： current status data change-point hazard model cure fraction pseudo-maximum likelihood

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