1
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Wang L, Wang L. An EM algorithm for analyzing right-censored survival data under the semiparametric proportional odds model. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2020.1837879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Lu Wang
- Department of Statistics, University of South Carolina, Columbia, South Carolina, USA
| | - Lianming Wang
- Department of Statistics, University of South Carolina, Columbia, South Carolina, USA
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2
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Feng Y, Wang J, Li Y. Goodness-of-fit inference for the additive hazards regression model with clustered current status data. J Appl Stat 2022. [DOI: 10.1080/02664763.2022.2053950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Yanqin Feng
- School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, People's Repubic of China
| | - Jie Wang
- School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, People's Repubic of China
| | - Yang Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
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3
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Wang L, Wang L. Regression analysis of arbitrarily censored survival data under the proportional odds model. Stat Med 2021; 40:3724-3739. [PMID: 33882618 DOI: 10.1002/sim.8994] [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] [Received: 07/01/2020] [Revised: 02/13/2021] [Accepted: 03/29/2021] [Indexed: 11/09/2022]
Abstract
Arbitrarily censored data are referred to as the survival data that contain a mixture of exactly observed, left-censored, interval-censored, and right-censored observations. Existing research work on regression analysis on arbitrarily censored data is relatively sparse and mainly focused on the proportional hazards model and the accelerated failure time model. This article studies the proportional odds (PO) model and proposes a novel estimation approach through an expectation-maximization (EM) algorithm for analyzing such data. The proposed EM algorithm has many appealing properties such as being robust to initial values, easy to implement, converging fast, and providing the variance estimate of the regression parameter estimate in closed form. An informal diagnosis plot is developed for checking the PO model assumption. Our method has shown excellent performance in estimating the regression parameters as well as the baseline survival function in a simulation study. A real-life dataset about metastatic colorectal cancer is analyzed for illustration. An R package regPO has been created for practitioners to implement our method.
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Affiliation(s)
- Lu Wang
- Department of Mathematics, Western New England University, Springfield, Massachusetts, USA
| | - Lianming Wang
- Department of Statistics, University of South Carolina, Columbia, South Carolina, USA
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4
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Pan C, Cai B, Wang L. A Bayesian approach for analyzing partly interval-censored data under the proportional hazards model. Stat Methods Med Res 2020; 29:3192-3204. [PMID: 32441211 DOI: 10.1177/0962280220921552] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Partly interval-censored time-to-event data often occur in biomedical studies of diseases where periodic medical examinations for symptoms of interest are necessary. Recent decades have seen blooming methods and R packages for interval-censored data; however, the research effort for partly interval-censored data is limited. We propose an efficient and easy-to-implement Bayesian semiparametric method for analyzing partly interval-censored data under the proportional hazards model. Two simulation studies are conducted to compare the performance of the proposed method with two main Bayesian methods currently available in the literature and the classic Cox proportional hazards model. The proposed method is applied to a partly interval-censored progression-free survival data from a metastatic colorectal cancer trial.
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Affiliation(s)
- Chun Pan
- Department of Mathematics and Statistics, Hunter College, New York, NY, USA
| | - Bo Cai
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
| | - Lianming Wang
- Department of Statistics, University of South Carolina, Columbia, SC, USA
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5
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Pan C, Cai B, Wang L, Lin X. ICBayes: a package of Bayesian semiparametric regression for intervel-censored data. COMMUN STAT-SIMUL C 2019. [DOI: 10.1080/03610918.2019.1687720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Chun Pan
- Department of Mathematics and Statistics, Hunter College, New York, New York, USA
| | - Bo Cai
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, South Carolina, USA
| | - Lianming Wang
- Department of Statistics, University of South Carolina, Columbia, South Carolina, USA
| | - Xiaoyan Lin
- Department of Statistics, University of South Carolina, Columbia, South Carolina, USA
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6
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Gamage PWW, McMahan CS, Wang L, Tu W. A Gamma-frailty proportional hazards model for bivariate interval-censored data. Comput Stat Data Anal 2019; 128:354-366. [PMID: 31011236 DOI: 10.1016/j.csda.2018.07.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Correlated survival data naturally arise from many clinical and epidemiological studies. For the analysis of such data, the Gamma-frailty proportional hazards (PH) model is a popular choice because the regression parameters have marginal interpretations and the statistical association between the failure times can be explicitly quantified via Kendall's tau. Despite their popularity, Gamma-frailty PH models for correlated interval-censored data have not received as much attention as analogous models for right-censored data. In this work, a Gamma-frailty PH model for bivariate interval-censored data is presented and an easy to implement expectation-maximization (EM) algorithm for model fitting is developed. The proposed model adopts a monotone spline representation for the purposes of approximating the unknown conditional cumulative baseline hazard functions, significantly reducing the number of unknown parameters while retaining modeling flexibility. The EM algorithm was derived from a data augmentation procedure involving latent Poisson random variables. Extensive numerical studies illustrate that the proposed method can provide reliable estimation and valid inference, and is moreover robust to the misspecification of the frailty distribution. To further illustrate its use, the proposed method is used to analyze data from an epidemiological study of sexually transmitted infections.
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Affiliation(s)
| | | | - Lianming Wang
- Department of Statistics, University of South Carolina, SC 29208, U.S.A
| | - Wanzhu Tu
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN 46202, U.S.A
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7
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Zhou H, Hanson T. A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data. J Am Stat Assoc 2018. [DOI: 10.1080/01621459.2017.1356316] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Haiming Zhou
- Division of Statistics, Northern Illinois University, DeKalb, IL
| | - Timothy Hanson
- Department of Statistics, University of South Carolina, Columbia, SC
- Medtronic Inc., Minneapolis, MN
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8
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Lin X, Cai B, Wang L, Zhang Z. A Bayesian proportional hazards model for general interval-censored data. LIFETIME DATA ANALYSIS 2015; 21:470-490. [PMID: 25098226 DOI: 10.1007/s10985-014-9305-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2013] [Accepted: 07/23/2014] [Indexed: 06/03/2023]
Abstract
The proportional hazards (PH) model is the most widely used semiparametric regression model for analyzing right-censored survival data based on the partial likelihood method. However, the partial likelihood does not exist for interval-censored data due to the complexity of the data structure. In this paper, we focus on general interval-censored data, which is a mixture of left-, right-, and interval-censored observations. We propose an efficient and easy-to-implement Bayesian estimation approach for analyzing such data under the PH model. The proposed approach adopts monotone splines to model the baseline cumulative hazard function and allows to estimate the regression parameters and the baseline survival function simultaneously. A novel two-stage data augmentation with Poisson latent variables is developed for the efficient computation. The developed Gibbs sampler is easy to execute as it does not require imputing any unobserved failure times or contain any complicated Metropolis-Hastings steps. Our approach is evaluated through extensive simulation studies and illustrated with two real-life data sets.
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Affiliation(s)
- Xiaoyan Lin
- Department of Statistics, University of South Carolina, Columbia, SC, 29208, USA,
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9
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Regression analysis of bivariate current status data under the Gamma-frailty proportional hazards model using the EM algorithm. Comput Stat Data Anal 2015. [DOI: 10.1016/j.csda.2014.10.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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10
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Li L, Hanson T, Zhang J. Spatial extended hazard model with application to prostate cancer survival. Biometrics 2014; 71:313-22. [PMID: 25521422 DOI: 10.1111/biom.12268] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2014] [Revised: 10/01/2014] [Accepted: 10/01/2014] [Indexed: 11/28/2022]
Abstract
This article develops a Bayesian semiparametric approach to the extended hazard model, with generalization to high-dimensional spatially grouped data. County-level spatial correlation is accommodated marginally through the normal transformation model of Li and Lin (2006, Journal of the American Statistical Association 101, 591-603), using a correlation structure implied by an intrinsic conditionally autoregressive prior. Efficient Markov chain Monte Carlo algorithms are developed, especially applicable to fitting very large, highly censored areal survival data sets. Per-variable tests for proportional hazards, accelerated failure time, and accelerated hazards are efficiently carried out with and without spatial correlation through Bayes factors. The resulting reduced, interpretable spatial models can fit significantly better than a standard additive Cox model with spatial frailties.
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Affiliation(s)
- Li Li
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico, U.S.A
| | - Timothy Hanson
- Department of Statistics, University of South Carolina, Columbia, South Carolina, U.S.A
| | - Jiajia Zhang
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, South Carolina, U.S.A
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11
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Gao F, Wellner JA. Global rates of convergence of the MLE for multivariate interval censoring. Electron J Stat 2013; 7:364-380. [PMID: 23991245 PMCID: PMC3753818 DOI: 10.1214/13-ejs777] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
We establish global rates of convergence of the Maximum Likelihood Estimator (MLE) of a multivariate distribution function on ℝ d in the case of (one type of) "interval censored" data. The main finding is that the rate of convergence of the MLE in the Hellinger metric is no worse than n-1/3(log n)γ for γ = (5d - 4)/6.
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Affiliation(s)
- Fuchang Gao
- Department of Mathematics, University of Idaho, Moscow, Idaho 83844-1103,
| | - Jon A. Wellner
- Department of Statistics, Box 354322, University of Washington, Seattle, WA 98195-4322, , url: http://www.stat.washington.edu/jaw/
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