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Ma R, Zhao S, Sun J, Wang S. Estimation of accelerated hazards models based on case K informatively interval-censored failure time data. J Appl Stat 2023; 51:1251-1270. [PMID: 38835825 PMCID: PMC11146267 DOI: 10.1080/02664763.2023.2196752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 03/23/2023] [Indexed: 04/08/2023]
Abstract
The accelerated hazards model is one of the most commonly used models for regression analysis of failure time data and this is especially the case when, for example, the hazard functions may have monotonicity property. Correspondingly a large literature has been established for its estimation or inference when right-censored data are observed. Although several methods have also been developed for its inference based on interval-censored data, they apply only to limited situations or rely on some assumptions such as independent censoring. In this paper, we consider the situation where one observes case K interval-censored data, the type of failure time data that occur most in, for example, medical research such as clinical trials or periodical follow-up studies. For inference, we propose a sieve borrow-strength method and in particular, it allows for informative censoring. The asymptotic properties of the proposed estimators are established. Simulation studies demonstrate that the proposed inference procedure performs well. The method is applied to a set of real data set arising from an AIDS clinical trial.
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Affiliation(s)
- Rui Ma
- Center for Applied Statistical Research and College of Mathematics, Jilin University, Changchun, People's Republic of China
| | - Shishun Zhao
- Center for Applied Statistical Research and College of Mathematics, Jilin University, Changchun, People's Republic of China
| | - Jianguo Sun
- Department of Statistics, University of Missouri, Columbia, MO, USA
| | - Shuying Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, People's Republic of China
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2
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Ma Y, Wang P, Li S, Sun J. Estimation of complier causal treatment effects under the additive hazards model with interval-censored data. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2022.2155791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Yuqing Ma
- School of Mathematics, Jilin University, Changchun, China
| | - Peijie Wang
- School of Mathematics, Jilin University, Changchun, China
| | - Shuwei Li
- School of Economics and Statistics, Guangzhou University, Guangzhou, China
| | - Jianguo Sun
- Department of Statistics, University of Missouri, Columbia, Missouri, USA
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Wang S, Xu D, Wang C, Sun J. Estimation of linear transformation cure models with informatively interval-censored failure time data. J Nonparametr Stat 2022. [DOI: 10.1080/10485252.2022.2148667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Shuying Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, People's Republic of Chin
| | - Da Xu
- Key Laboratory of Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, People's Republic of China
| | - Chunjie Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, People's Republic of Chin
| | - Jianguo Sun
- Department of Statistics, University of Missouri, Columbia, MO, USA
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4
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Zhao B, Wang S, Wang C. Efficient estimation for accelerated failure time model with interval-censored data in the presence of a cured subgroup. COMMUN STAT-SIMUL C 2022. [DOI: 10.1080/03610918.2022.2118780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Bo Zhao
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, China
| | - Shuying Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, China
| | - Chunjie Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, China
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Wang S, Wang C, Song X, Xu D. Joint analysis of informatively interval-censored failure time and panel count data. Stat Methods Med Res 2022; 31:2054-2068. [PMID: 35818765 DOI: 10.1177/09622802221111559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Interval-censored failure time and panel count data, which frequently arise in medical studies and social sciences, are two types of important incomplete data. Although methods for their joint analysis have been available in the literature, they did not consider the observation process, which may depend on the failure time and/or panel count of interest. This study considers a three-component joint model to analyze interval-censored failure time, panel counts, and the observation process within a unique framework. Gamma and distribution-free frailties are introduced to jointly model the interdependency among the interval-censored data, panel count data, and the observation process. We propose a sieve maximum likelihood approach coupled with Bernstein polynomial approximation to estimate the unknown parameters and baseline hazard function. The asymptotic properties of the resulting estimators are established. An extensive simulation study suggests that the proposed procedure works well for practical situations. An application of the method to a real-life dataset collected from a cardiac allograft vasculopathy study is presented.
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Affiliation(s)
- Shuying Wang
- School of Mathematics and Statistics, 177552Changchun University of Technology, Changchun, People's Republic of China
| | - Chunjie Wang
- School of Mathematics and Statistics, 177552Changchun University of Technology, Changchun, People's Republic of China
| | - Xinyuan Song
- Department of Statistics, 26451The Chinese University of Hong Kong, Shatin, NT, Hong Kong
| | - Da Xu
- Key Laboratory of Applied Statistics of MOE and School of Mathematics and Statistics, 47821Northeast Normal University, Changchun, People's Republic of China
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Yu M, Feng Y, Duan R, Sun J. Regression analysis of multivariate interval-censored failure time data with informative censoring. Stat Methods Med Res 2021; 31:391-403. [PMID: 34878352 DOI: 10.1177/09622802211061668] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Regression analysis of multivariate interval-censored failure time data has been discussed by many authors1-6. For most of the existing methods, however, one limitation is that they only apply to the situation where the censoring is non-informative or the failure time of interest is independent of the censoring mechanism. It is apparent that this may not be true sometimes and as pointed out by some authors, the analysis that does not take the dependent censoring into account could lead to biased or misleading results7,8. In this study, we consider regression analysis of multivariate interval-censored data arising from the additive hazards model and propose an estimating equation-based approach that allows for the informative censoring. The method can be easily implemented and the asymptotic properties of the proposed estimator of regression parameters are established. Also we perform a simulation study for the evaluation of the proposed method and it suggests that the method works well for practical situations. Finally, the proposed approach is applied to a set of real data.
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Affiliation(s)
- Mengzhu Yu
- Center for Applied Statistical Research and College of Mathematics, 12510Jilin University, China
| | - Yanqin Feng
- School of Mathematics and Statistics, 12390Wuhan University, China
| | | | - Jianguo Sun
- Department of Statistics, 2628University of Missouri, USA
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Zhao B, Wang S, Wang C, Sun J. New methods for the additive hazards model with the informatively interval-censored failure time data. Biom J 2021; 63:1507-1525. [PMID: 34216403 DOI: 10.1002/bimj.202000288] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 02/17/2021] [Accepted: 04/07/2021] [Indexed: 11/06/2022]
Abstract
The additive hazards model is one of the most commonly used models for regression analysis of failure time data and many inference procedures have been developed for it under various situations. In particular, Wang et al. (2018a, Computational Statistics and Data Analysis, 125, 1-9) discussed the situation where one observes informatively interval-censored data and proposed a likelihood estimation approach. However , it involves estimation of the unknown baseline cumulative hazard function and thus may be time-consuming . Corresponding to this, we propose two new procedures, an estimating equation-based one and an empirical likelihood-based one, and both do not need estimation of the cumulative hazard function and can be easily implemented. The asymptotic properties of the proposed methods are established and an extensive simulation study suggests that they work well in practical situations. An application is also provided.
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Affiliation(s)
- Bo Zhao
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, Jilin, P. R. China
| | - Shuying Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, Jilin, P. R. China
| | - Chunjie Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, Jilin, P. R. China
| | - Jianguo Sun
- Department of Statistics, University of Missouri, Columbia, MO, USA
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Wang S, Wang C, Sun J. An additive hazards cure model with informative interval censoring. LIFETIME DATA ANALYSIS 2021; 27:244-268. [PMID: 33481146 DOI: 10.1007/s10985-021-09515-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 01/03/2021] [Indexed: 06/12/2023]
Abstract
The existence of a cured subgroup happens quite often in survival studies and many authors considered this under various situations (Farewell in Biometrics 38:1041-1046, 1982; Kuk and Chen in Biometrika 79:531-541, 1992; Lam and Xue in Biometrika 92:573-586, 2005; Zhou et al. in J Comput Graph Stat 27:48-58, 2018). In this paper, we discuss the situation where only interval-censored data are available and furthermore, the censoring may be informative, for which there does not seem to exist an established estimation procedure. For the analysis, we present a three component model consisting of a logistic model for describing the cure rate, an additive hazards model for the failure time of interest and a nonhomogeneous Poisson model for the observation process. For estimation, we propose a sieve maximum likelihood estimation procedure and the asymptotic properties of the resulting estimators are established. Furthermore, an EM algorithm is developed for the implementation of the proposed estimation approach, and extensive simulation studies are conducted and suggest that the proposed method works well for practical situations. Also the approach is applied to a cardiac allograft vasculopathy study that motivated this investigation.
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Affiliation(s)
- Shuying Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, 130012, China
| | - Chunjie Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, 130012, China.
| | - Jianguo Sun
- Center for Applied Statistical Research, School of Mathematics, Jilin University, Changchun, 130012, China
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Wang S, Xu D, Wang C, Sun J. Semiparametric analysis of case K interval-censored failure time data in the presence of a cured subgroup and informative censoring. J STAT COMPUT SIM 2021. [DOI: 10.1080/00949655.2021.1880587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Shuying Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, People's Republic of China
| | - Da Xu
- Key Laboratory of Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, People's Republic of China
| | - Chunjie Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, People's Republic of China
| | - Jianguo Sun
- Department of Statistics, University of Missouri, Columbia, MO, USA
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Zhu Y, Chen Z, Lawless JF. Semiparametric analysis of interval‐censored failure time data with outcome‐dependent observation schemes. Scand Stat Theory Appl 2021. [DOI: 10.1111/sjos.12511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Yayuan Zhu
- Department of Epidemiology and Biostatistics University of Western Ontario London Ontario Canada
| | - Ziqi Chen
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science‐MOE, School of Statistics East China Normal University Shanghai P.R. China
| | - Jerald F. Lawless
- Department of Statistics and Actuarial Science University of Waterloo Waterloo Ontario Canada
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Du M, Zhou Q, Zhao S, Sun J. Regression Analysis of Case-cohort Studies in the Presence of Dependent Interval Censoring. J Appl Stat 2020; 48:846-865. [PMID: 33767519 PMCID: PMC7986575 DOI: 10.1080/02664763.2020.1752633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 04/01/2020] [Indexed: 10/24/2022]
Abstract
The case-cohort design is widely used as a means of reducing the cost in large cohort studies, especially when the disease rate is low and covariate measurements may be expensive, and has been discussed by many authors. In this paper, we discuss regression analysis of case-cohort studies that produce interval-censored failure time with dependent censoring, a situation for which there does not seem to exist an established approach. For inference, a sieve inverse probability weighting estimation procedure is developed with the use of Bernstein polynomials to approximate the unknown baseline cumulative hazard functions. The proposed estimators are shown to be consistent and the asymptotic normality of the resulting regression parameter estimators are established. A simulation study is conducted to assess the finite sample properties of the proposed approach and indicates that it works well in practical situations. The proposed method is applied to an HIV/AIDS case-cohort study that motivated this investigation.
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Affiliation(s)
- Mingyue Du
- Center for Applied Statistical Research and College of Mathematics, Jilin University, Changchun, People's Republic of China
| | - Qingning Zhou
- Department of Mathematics and Statistics, The University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Shishun Zhao
- Center for Applied Statistical Research and College of Mathematics, Jilin University, Changchun, People's Republic of China
| | - Jianguo Sun
- Department of Statistics, University of Missouri, Columbia, MO, USA
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Wang S, Wang C, Wang P, Sun J. Estimation of the additive hazards model with case K interval-censored failure time data in the presence of informative censoring. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2019.106891] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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