期刊：
Communications in Statistics - Theory and Methods,2023年52(15):5470-5482 ISSN：0361-0926
通讯作者：
Zhao, Hui
作者机构：
[Luo, Lin] Cent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China.;[Zhao, Hui] Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430073, Peoples R China.
通讯机构：
[Zhao, Hui] Z;Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430073, Peoples R China.
关键词：
Estimating equation;propensity score;linear transformation model;interval-censored data
摘要：
Interval-censored failure time data often occur in medical follow-up studies among other areas. Regression analysis of linear transformation models with interval-censored data has been investigated by several authors under different contexts, but most of the existing methods assume that the covariates are discrete because these methods rely on the estimation of conditional survival distribution function. Without this assumption, this paper constructs a new generalized estimating equation using the propensity score. The proposed inference procedure does not need to estimate the conditional survival distribution any more and then can be used not only in the discrete but also in the continuous covariate situation. The asymptotic properties of the resulting estimates are given, and an extensive simulation study is performed. Finally, the application to two real datasets is also provided. Key words: Estimating equation; Interval-censored data; Propensity score; Linear transformation model.
摘要：
Clustered interval-censored failure time data often occur in a wide variety of research and application fields such as cancer and AIDS studies. For such data, the failure times of interest are interval-censored and may be correlated for subjects coming from the same cluster. This paper presents a robust semiparametric transformation mixed effect models to analyze such data and use a U-statistic based on rank correlation to estimate the unknown parameters. The large sample properties of the estimator are also established. In addition, the authors illustrate the performance of the proposed estimate with extensive simulations and two real data examples.
作者：
Fatma Hashem Essawe;Mohamed Abd Elgawad;Haroon Mohamed Barakat;Hui Zhao
期刊：
Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences.,2020年73(6):525-532 ISSN：1407-009X
通讯作者：
Elgawad, M.A.
作者机构：
[Mohamed Abd Elgawad] Faculty of Science , Benha University 13518 , Egypt;[Hui Zhao] School of Mathematics and Statistics , Central China Normal University , Wuhan 430079 , China;Faculty of Applied Science , Red Sea University , Sudan;[Haroon Mohamed Barakat] Faculty of Science , Zagazig University 44519 , Egypt;[Fatma Hashem Essawe] School of Mathematics and Statistics , Central China Normal University , Wuhan 430079 , China<&wdkj&>Faculty of Applied Science , Red Sea University , Sudan
通讯机构：
Faculty of Science, Benha University, Egypt
关键词：
Bivariate generalised order statistics;Gaussian sequences;Generalised order statistics;Generalised quasi-range;Random indices;Weak convergence
摘要：
<jats:title>Abstract</jats:title>
<jats:p>In this paper, we study the limit distribution functions of the (lower-lower), (upper-upper) and (lower-upper) extreme and central-central <jats:italic>m</jats:italic>-generalised order statistics (m–GOS) of stationary Gaussian sequences under an equi-correlated set up, when the random sample size is assumed to converge weakly and independent of the basic variables. Moreover, sufficient conditions for a weak convergence of generalised quasi-range with random indices are obtained.</jats:p>
作者机构：
[Qin, Hong; Jiang, Qin] Cent China Normal Univ, Dept Stat, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Qin] Huanggang Normal Univ, Dept Math, Huanggang 438000, Peoples R China.;[Zhao, Hui] Zhongnan Univ Econ & Law, Dept Stat, Wuhan 430073, Hubei, Peoples R China.
通讯机构：
[Jiang, Qin] C;[Jiang, Qin] H;Cent China Normal Univ, Dept Stat, Wuhan 430079, Hubei, Peoples R China.;Huanggang Normal Univ, Dept Math, Huanggang 438000, Peoples R China.
关键词：
gap times;model checking;recurrent events;estimating equations;semiparametric model
摘要：
In the article, we investigate a general class of semiparametric hazards regression models for recurrent gap times. The general class includes the proportional hazards model, the accelerated failure time model and the accelerated hazards models as special cases. The model is flexible in modelling recurrent gap times since a covariate effect is identified as having two separate components, namely a time-scale change on hazard progression and a relative hazards ratio. In order to infer the model parameters, the procedure is proposed based on estimating equations. The asymptotic properties of the proposed estimators are established and the finite sample properties are investigated via simulation studies. In addition, a lack of fit test is presented to assess the adequacy of the model and an application of data from a bladder cancer study is reported for illustration.
期刊：
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE,2018年46(3):416-428 ISSN：0319-5724
通讯作者：
Sun, Jianguo
作者机构：
[Zhao, Hui] Cent China Normal Univ, Sch Math & Stat, Wuhan, Hubei, Peoples R China.;[Zhao, Hui] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan, Hubei, Peoples R China.;[Sun, Dayu; Sun, Jianguo] Univ Missouri, Dept Stat, Columbia, MO 65211 USA.;[Li, Gang] Univ Calif Los Angeles, Dept Biostat, Los Angeles, CA USA.
通讯机构：
[Sun, Jianguo] U;Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
关键词：
Additive rate model;event history study;recurrent event data;variable selection
摘要：
Recurrent event data occur in many areas such as medical studies and social sciences and a great deal of literature has been established for their analysis. On the other hand, only limited research exists on the variable selection for recurrent event data, and the existing methods can be seen as direct generalizations of the available penalized procedures for linear models and may not perform as well as expected. This article discusses simultaneous parameter estimation and variable selection and presents a new method with a new penalty function, which will be referred to as the broken adaptive ridge regression approach. In addition to the establishment of the oracle property, we also show that the proposed method has the clustering or grouping effect when covariates are highly correlated. Furthermore, a numerical study is performed and indicates that the method works well for practical situations and can outperform existing methods. An application is provided. The Canadian Journal of Statistics 46: 416-428; 2018 (c) 2018 Statistical Society of Canada Une riche litterature traite de l'analyse des evenements recurrents, un type de donnees observe notamment dans les etudes medicales et dans les projets de recherche en sciences sociales. Par contre, peu de resultats de recherche portent sur la selection de variables pour ces modeles. Les methodes existantes peuvent etre vues comme une generalisation directe de procedures penalisees disponibles pour les modeles lineaires et peuvent offrir des performances inferieures aux attentes. Les auteurs proposent l'approche de regression ridge brisee adaptative oU ils procedent simultanement a l'estimation de parametres et a la selection de variables en exploitant une nouvelle fonction de penalite. Ils prouvent la propriete d'oracle de leur methode et montrent qu'elle possede une propriete de regroupement lorsque les covariables sont hautement correlees. Ils presentent une etude numerique qui indique que leur methode fonctionne bien dans des situations pratiques et peut meme s'averer plus performante que les approches existantes. Ils fournissent egalement un exemple d'application. La revue canadienne de statistique 46: 416-428; 2018 (c) 2018 Societestatistique du Canada
期刊：
Lifetime Data Analysis,2018年24(1):94-109 ISSN：1380-7870
通讯作者：
Sun, Jianguo
作者机构：
[Xu, Da; Sun, Jianguo] Jilin Univ, Sch Math, Ctr Appl Stat Res, Changchun 130012, Jilin, Peoples R China.;[Zhao, Hui] Cent China Normal Univ, Dept Stat, Wuhan 430079, Hubei, Peoples R China.;[Sun, Jianguo] Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
通讯机构：
[Sun, Jianguo] J;[Sun, Jianguo] U;Jilin Univ, Sch Math, Ctr Appl Stat Res, Changchun 130012, Jilin, Peoples R China.;Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
关键词：
Bernstein polynomial;Event history study;Frailty model;Sieve maximum likelihood estimation
摘要：
Interval-censored failure time data and panel count data are two types of incomplete data that commonly occur in event history studies and many methods have been developed for their analysis separately (Sun in The statistical analysis of interval-censored failure time data. Springer, New York, 2006; Sun and Zhao in The statistical analysis of panel count data. Springer, New York, 2013). Sometimes one may be interested in or need to conduct their joint analysis such as in the clinical trials with composite endpoints, for which it does not seem to exist an established approach in the literature. In this paper, a sieve maximum likelihood approach is developed for the joint analysis and in the proposed method, Bernstein polynomials are used to approximate unknown functions. The asymptotic properties of the resulting estimators are established and in particular, the proposed estimators of regression parameters are shown to be semiparametrically efficient. In addition, an extensive simulation study was conducted and the proposed method is applied to a set of real data arising from a skin cancer study.
期刊：
Journal of Nonparametric Statistics,2018年30(3):703-715 ISSN：1048-5252
通讯作者：
Sun, Jianguo
作者机构：
[Zhao, Hui] Cent China Normal Univ, Sch Math & Stat, Wuhan, Hubei, Peoples R China.;[Zhao, Hui] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan, Hubei, Peoples R China.;[Ma, Chenchen; Li, Junlong; Sun, Jianguo] Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
通讯机构：
[Sun, Jianguo] U;Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
摘要：
This paper discusses regression analysis of clustered interval-censored failure time data, which often occur in medical follow-up studies among other areas. For such data, sometimes the failure time may be related to the cluster size, the number of subjects within each cluster or we have informative cluster sizes. For the problem, we present a within-cluster resampling method for the situation where the failure time of interest can be described by a class of linear transformation models. In addition to the establishment of the asymptotic properties of the proposed estimators of regression parameters, an extensive simulation study is conducted for the assessment of the finite sample properties of the proposed method and suggests that it works well in practical situations. An application to the example that motivated this study is also provided.
期刊：
STATISTICS AND ITS INTERFACE,2018年11(3):463-471 ISSN：1938-7989
通讯作者：
Sun, Jianguo
作者机构：
[Cui, Qi; Sun, Jianguo] Jilin Univ, Sch Math, Changchun 130012, Jilin, Peoples R China.;[Zhao, Hui] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China.;[Zhao, Hui] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan 430079, Hubei, Peoples R China.;[Sun, Jianguo] Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
通讯机构：
[Sun, Jianguo] J;[Sun, Jianguo] U;Jilin Univ, Sch Math, Changchun 130012, Jilin, Peoples R China.;Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
关键词：
Copula model;Current status data;Informative censoring;Proportional hazards model
期刊：
Journal of Nonparametric Statistics,2018年30(3):758-773 ISSN：1048-5252
通讯作者：
Zhao, Hui
作者机构：
[Wang, Peijie] Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China.;[Wang, Peijie; Du, Mingyue] Jilin Univ, Ctr Appl Stat Res, Sch Math, Changchun, Jilin, Peoples R China.;[Zhao, Hui] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China.;[Sun, Jianguo] Univ Missouri, Dept Stat, Columbiaville, MI USA.
通讯机构：
[Zhao, Hui] C;Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China.
关键词：
Case K interval-censored data;informative censoring;model checking;sieve maximum likelihood estimation;transformation model
摘要：
Failure time data occur in many areas and in various censoring forms and many models have been proposed for their regression analysis such as the proportional hazards model and the proportional odds model. Another choice that has been discussed in the literature is a general class of semiparmetric transformation models, which include the two models above and many others as special cases. In this paper, we consider this class of models when one faces a general type of censored data, case K informatively interval-censored data, for which there does not seem to exist an established inference procedure. For the problem, we present a two-step estimation procedure that is quite flexible and can be easily implemented, and the consistency and asymptotic normality of the proposed estimators of regression parameters are established. In addition, an extensive simulation study is conducted and suggests that the proposed procedure works well for practical situations. An application is also provided.
期刊：
Journal of the Indian Society for Probability and Statistics,2018年19(2):359-377 ISSN：2364-9569
通讯作者：
A. M. Elsawah
作者机构：
Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt;Division of Science and Technology, BNU-HKBU United International College, Zhuhai, China;[Hui Zhao] Faculty of Mathematics and Statistics, Central China Normal University, Wuhan, China;Department of Statistics and Computer, Faculty of Applied Science, Read Sea University, Port Sudan, Sudan;[A. M. Elsawah] Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt<&wdkj&>Division of Science and Technology, BNU-HKBU United International College, Zhuhai, China
通讯机构：
[A. M. Elsawah] D;Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt<&wdkj&>Division of Science and Technology, BNU-HKBU United International College, Zhuhai, China
关键词：
Dual generalized order statistics;Asymptotic theory;Homogeneous population;Heterogeneous population;Normalization
摘要：
The outcomes of several real-life experiments arise in descending order. Dual generalized order statistics (DGOS) have been introduced as a unification of several models of descendingly ordered random variables like reversed ordered order statistics, lower k-records and lower Pfeifer records. The asymptotic theory (AT) proceeds by assuming that it is possible (in principle) to keep collecting additional data, so that the sample size grows infinitely. Under this assumption, many results can be obtained that are unavailable for samples of finite size. The AT is widely used in various statistical approaches, such as ordered random variables, time series models, estimation, testing hypotheses and so on. While the AT of DGOS from homogeneous population, i.e., all of the data points come from the same distribution, has been soundly investigated, no research has been devoted to this problem for heterogeneous population, i.e., the data points come from more than one distribution. This paper gives a closer look at the AT of DGOS based on data from a finite mixture of distributions normalized by the same continuous strictly monotonic sequence or a mixture of continuous strictly monotonic sequences.
作者机构：
[Wang, Peijie] Jilin Univ, Sch Math, Changchun 130012, Peoples R China.;[Zhao, Hui] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;[Zhao, Hui] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan 430079, Peoples R China.;[Sun, Jianguo] Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
关键词：
Case K interval-censored data;Informative censoring;Proportional hazards model;Sieve maximum-likelihood estimation
摘要：
Interval-censored failure time data occur in many fields such as demography, economics, medical research, and reliability and many inference procedures on them have been developed (Sun, 2006; Chen, Sun, and Peace, 2012). However, most of the existing approaches assume that the mechanism that yields interval censoring is independent of the failure time of interest and it is clear that this may not be true in practice (Zhang et al., 2007; Ma, Hu, and Sun, 2015). In this article, we consider regression analysis of case K interval-censored failure time data when the censoring mechanism may be related to the failure time of interest. For the problem, an estimated sieve maximum-likelihood approach is proposed for the data arising from the proportional hazards frailty model and for estimation, a two-step procedure is presented. In the addition, the asymptotic properties of the proposed estimators of regression parameters are established and an extensive simulation study suggests that the method works well. Finally, we apply the method to a set of real interval-censored data that motivated this study.
摘要：
Interval-censored failure time data occur in many fields including epidemiological and medical studies as well as financial and sociological studies, and many authors have investigated their analysis (Sun, The statistical analysis of interval-censored failure time data, 2006; Zhang, Stat Modeling 9:321-343, 2009). In particular, a number of procedures have been developed for regression analysis of interval-censored data arising from the proportional hazards model (Finkelstein, Biometrics 42:845-854, 1986; Huang, Ann Stat 24:540-568, 1996; Pan, Biometrics 56:199-203, 2000). For most of these procedures, however, one drawback is that they involve estimation of both regression parameters and baseline cumulative hazard function. In this paper, we propose two simple estimation approaches that do not need estimation of the baseline cumulative hazard function. The asymptotic properties of the resulting estimates are given, and an extensive simulation study is conducted and indicates that they work well for practical situations.
作者：
Zhu, Liang*;Zhao, Hui;Sun, Jianguo;Leisenring, Wendy;Robison, Leslie L.
期刊：
Biometrics,2015年71(1):71-79 ISSN：0006-341X
通讯作者：
Zhu, Liang
作者机构：
[Zhu, Liang] St Jude Childrens Res Hosp, Dept Biostat, Memphis, TN 38103 USA.;[Zhao, Hui] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;[Sun, Jianguo] Univ Missouri, Dept Stat, Columbia, MO 65211 USA.;[Sun, Jianguo] Jilin Univ, Sch Math, Changchun 130012, Peoples R China.;[Leisenring, Wendy] Fred Hutchinson Canc Res Ctr, Dept Biostat, Seattle, WA 98109 USA.
通讯机构：
[Zhu, Liang] S;St Jude Childrens Res Hosp, Dept Biostat, Memphis, TN 38103 USA.
关键词：
Additive rate model;Event-history studies;Mixed data;Panel-count data;Recurrent-event data
摘要：
<jats:title>Summary</jats:title>
<jats:p>Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007, The Statistical Analysis of Recurrent Events. New York: Springer-Verlag; Zhao et al., 2011, Test 20, 1–42). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013, Statistics in Medicine 32, 1954–1963). In this article, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study.</jats:p>
作者：
Zhu, Liang*;Zhao, Hui;Sun, Jianguo;Pounds, Stanley
期刊：
Communications in Statistics - Theory and Methods,2014年43(23):4998-5011 ISSN：0361-0926
通讯作者：
Zhu, Liang
作者机构：
[Zhu, Liang; Pounds, Stanley] St Jude Childrens Res Hosp, Dept Biostat, Memphis, TN 38103 USA.;[Zhao, Hui] Cent China Normal Univ, Sch Math & Stat, Wuhan, Hubei Province, Peoples R China.;[Sun, Jianguo] Univ Missouri, Dept Stat, Columbia, MO 65211 USA.;[Sun, Jianguo] Jilin Univ, Changchun 130023, Peoples R China.
通讯机构：
[Zhu, Liang] S;St Jude Childrens Res Hosp, Dept Biostat, Memphis, TN 38103 USA.
关键词：
Conditional model;Longitudinal data analysis;Recurrent episode process
摘要：
Recently, there has been a great interest in the analysis of longitudinal data in which the observation process is related to the longitudinal process. In literature, the observation process was commonly regarded as a recurrent event process. Sometimes some observation duration may occur and this process is referred to as a recurrent episode process. The medical cost related to hospitalization is an example. We propose a conditional modeling approach that takes into account both informative observation process and observation duration. We conducted simulation studies to assess the performance of the method and applied it to a dataset of medical costs.
期刊：
Communications in Statistics - Theory and Methods,2014年43(3):644-655 ISSN：0361-0926
通讯作者：
Sun, Jianguo
作者机构：
[Zhao, Hui] Cent China Normal Univ, Dept Stat, Wuhan, Peoples R China.;[Zhao, Hui; Virkler, Kate; Sun, Jianguo] Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
通讯机构：
[Sun, Jianguo] U;Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
关键词：
Counting processes;Medical follow-up study;Nonparametric comparison;Panel count data;Skin cancer study
摘要：
Multivariate panel count data often occur when there exist several related recurrent events or response variables defined by occurrences of related events. For univariate panel count data, several nonparametric treatment comparison procedures have been developed. However, it does not seem to exist a nonparametric procedure for multivariate cases. Based on differences between estimated mean functions, this article proposes a class of nonparametric test procedures for multivariate panel count data. The asymptotic distribution of the new test statistics is established and a simulation study is conducted. Moreover, the new procedures are applied to a skin cancer problem that motivated this study.
期刊：
Journal of Multivariate Analysis,2013年119:71-80 ISSN：0047-259X
通讯作者：
Zhao, Hui
作者机构：
[Zhang, Haixiang; Wang, Dehui; Sun, Jianguo] Jilin Univ, Sch Math, Changchun 130012, Peoples R China.;[Zhao, Hui] Cent China Normal Univ, Dept Stat, Wuhan 430079, Peoples R China.;[Sun, Jianguo] Univ Missouri, Dept Stat, Columbia, MO 65211 USA.;[Kim, KyungMann] Univ Wisconsin, Dept Biostat & Med Informat, Madison, WI 53792 USA.;[Kim, KyungMann] Univ Wisconsin, Dept Stat, Madison, WI 53792 USA.
通讯机构：
[Zhao, Hui] C;Cent China Normal Univ, Dept Stat, Wuhan 430079, Peoples R China.
关键词：
Estimating equation;Informative observation process;Marginal mean model;Model checking
摘要：
Multivariate panel count data arise in event history studies on recurrent events if there exist several related events and study subjects can be examined or observed only at discrete time points instead of over continuous periods. In these situations, a complicated issue that may arise is that the observation time points or process may be related to the underlying recurrent event process of interest. That is, we have informative observation processes. It is obvious that to perform a valid analysis, both the relationship among different types of recurrent events and the informative observation process need to be taken into account. To address these, we propose a robust joint modeling approach. For the estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Numerical studies indicate that the proposed approach works well for practical situations and the methodology is applied to a skin cancer study that motivates this study. (C) 2013 Elsevier Inc. All rights reserved.
期刊：
Journal of Nonparametric Statistics,2013年25(2):379-394 ISSN：1048-5252
通讯作者：
Sun, Jianguo
作者机构：
[Zhao, Hui] Cent China Normal Univ, Dept Stat, Wuhan 430079, Peoples R China.;[Li, Yang; Sun, Jianguo] Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
通讯机构：
[Sun, Jianguo] U;Univ Missouri, Dept Stat, Columbia, MO 65211 USA.
摘要：
This paper considers regression analysis of multivariate panel count data in the presence of some terminal events. Furthermore, both the observation process and the terminal event may be correlated with the recurrent event process of interest. For the problem, we present a semiparametric additive model for the mean function of the recurrent event process and an estimating equation-based inference procedure is developed for the estimation of regression parameters. In the procedure, the inverse survival probability weighting technique is used and the asymptotic properties of the proposed estimators are established. Extensive simulation studies are conducted to evaluate the finite sample properties of the proposed approach, and the results show that the proposed procedures work well for practical situations.
作者机构：
[Zhang, Hui; Zhu, Liang; Pounds, Stanley] St Jude Childrens Res Hosp, Dept Biostat, Memphis, TN 38103 USA.;[Zhao, Hui] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei Province, Peoples R China.;[Sun, Jianguo] Univ Missouri, Dept Stat, Columbia, MO 65211 USA.;[Sun, Jianguo] Jilin Univ, Sch Math, Changchun 130012, Peoples R China.
通讯机构：
[Zhao, Hui] C;Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei Province, Peoples R China.
关键词：
Joint model;Longitudinal data analysis;Random effect;Recurrent episode process
摘要：
This paper discusses regression analysis of longitudinal data in which the observation process may be related to the longitudinal process of interest. Such data have recently attracted a great deal of attention and some methods have been developed. However, most of those methods treat the observation process as a recurrent event process, which assumes that one observation can immediately follow another. Sometimes, this is not the case, as there may be some delay or observation duration. Such a process is often referred to as a recurrent episode process. One example is the medical cost related to hospitalization, where each hospitalization serves as a single observation. For the problem, we present a joint analysis approach for regression analysis of both longitudinal and observation processes and a simulation study is conducted that assesses the finite sample performance of the approach. The asymptotic properties of the proposed estimates are also given and the method is applied to the medical cost data that motivated this study.
