1
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Prataviera F, Hashimoto EM, Ortega EMM, Cordeiro GM, Cancho VG, Vila R. A new flexible regression model with application to recovery probability Covid-19 patients. J Appl Stat 2023; 51:826-844. [PMID: 38524797 PMCID: PMC10956937 DOI: 10.1080/02664763.2022.2163229] [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: 06/17/2022] [Accepted: 12/22/2022] [Indexed: 01/06/2023]
Abstract
The aim of this study is to propose a generalized odd log-logistic Maxwell mixture model to analyze the effect of gender and age groups on lifetimes and on the recovery probabilities of Chinese individuals with COVID-19. We add new properties of the generalized Maxwell model. The coefficients of the regression and the recovered fraction are estimated by maximum likelihood and Bayesian methods. Further, some simulation studies are done to compare the regressions for different scenarios. Model-checking techniques based on the quantile residuals are addressed. The estimated survival functions for the patients are reported by age range and sex. The simulation study showed that mean squared errors decay toward zero and the average estimates converge to the true parameters when sample size increases. According to the fitted model, there is a significant difference only in the age group on the lifetime of individuals with COVID-19. Women have higher probability of recovering than men and individuals aged ≥ 60 years have lower recovered probabilities than those who aged < 60 years. The findings suggest that the proposed model could be a good alternative to analyze censored lifetime of individuals with COVID-19.
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Affiliation(s)
- F. Prataviera
- Department of Exact Sciences, University of S ao Paulo, Piracicaba, Brazil
| | - E. M. Hashimoto
- Academic Department of Mathematics, Federal University of Technology – Paraná, Londrina, Brazil
| | - E. M. M. Ortega
- Department Exact Sciences, University of São Paulo, Piracicaba, Brazil
| | - G. M. Cordeiro
- Department of Statistics, Federal University of Pernambuco, Recife, Brazil
| | - V. G. Cancho
- Department of Statistics, University of São Paulo, São Carlos, Brazil
| | - R. Vila
- Department of Statistics, University of Brasilia, Brasilia, Brazil
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2
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do Espirito Santo APJ, Cancho VG, Louzada F, Ortega EMM. A survival model for lifetime with long-term survivors and unobserved heterogeneity. BRAZ J PROBAB STAT 2022. [DOI: 10.1214/22-bjps549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Vicente G. Cancho
- Institute for Mathematical Science and Computing, University of São Paulo, São Carlos, São Paulo, Brazil
| | - Francisco Louzada
- Institute for Mathematical Science and Computing, University of São Paulo, São Carlos, São Paulo, Brazil
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3
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Rocha JB, Medeiros FMC, Valença DM. Log-symmetric models with cure fraction with application to leprosy reactions data. BRAZ J PROBAB STAT 2022. [DOI: 10.1214/22-bjps540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Joyce B. Rocha
- Department of Statistics, Federal University of Rio Grande do Norte, UFRN, Natal, Brazil
| | | | - Dione M. Valença
- Department of Statistics, Federal University of Rio Grande do Norte, UFRN, Natal, Brazil
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4
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A survival regression with cure fraction applied to cervical cancer. Comput Stat 2022. [DOI: 10.1007/s00180-022-01233-4] [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]
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5
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Rahmati M, Rezanejad Asl P, Mikaeli J, Zeraati H, Rasekhi A. Compound Poisson frailty model with a gamma process prior for the baseline hazard: accounting for a cured fraction. J Appl Stat 2021; 49:3377-3391. [DOI: 10.1080/02664763.2021.1947997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Maryam Rahmati
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Reproductive Endocrinology Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Parisa Rezanejad Asl
- Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Javad Mikaeli
- Autoimmune and Motility Disorders Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hojjat Zeraati
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Aliakbar Rasekhi
- Biostatistics Department, Medical Sciences Faculty, Tarbiat Modares University, Tehran, Iran
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6
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Leão J, Bourguignon M, Saulo H, Santos-Neto M, Calsavara V. The Negative Binomial Beta Prime Regression Model with Cure Rate: Application with a Melanoma Dataset. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2021. [DOI: 10.1007/s42519-021-00195-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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7
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Wang Y, Zhang J, Cai C, Lu W, Tang Y. Semiparametric estimation for proportional hazards mixture cure model allowing non-curable competing risk. J Stat Plan Inference 2021. [DOI: 10.1016/j.jspi.2020.06.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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8
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Cancho VG, Barriga GDC, Cordeiro GM, Ortega EMM, Suzuki AK. Bayesian survival model induced by frailty for lifetime with long‐term survivors. STAT NEERL 2021. [DOI: 10.1111/stan.12236] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Vicente G. Cancho
- Departamento de Matemática Aplicada e Estatística ICMC, Universidade de São Paulo São Carlos Brazil
| | - Gladys D. C. Barriga
- Departamento de Engenharia de Produção FEB, Universidade Estadual Paulista Bauru Brazil
| | - Gauss M. Cordeiro
- Departamento de Estatística CCEN, Universidade Federal de Pernambuco Recife Brazil
| | - Edwin M. M. Ortega
- Departamento de Ciências Exatas ESALQ, Universidade de São Paulo Piracicaba Brazil
| | - Adriano K. Suzuki
- Departamento de Matemática Aplicada e Estatística ICMC, Universidade de São Paulo São Carlos Brazil
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9
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Karamoozian A, Baneshi MR, Bahrampour A. Bayesian mixture cure rate frailty models with an application to gastric cancer data. Stat Methods Med Res 2020; 30:731-746. [PMID: 33243085 DOI: 10.1177/0962280220974699] [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: 11/17/2022]
Abstract
Mixture cure rate models are commonly used to analyze lifetime data with long-term survivors. On the other hand, frailty models also lead to accurate estimation of coefficients by controlling the heterogeneity in survival data. Gamma frailty models are the most common models of frailty. Usually, the gamma distribution is used in the frailty random variable models. However, for survival data which are suitable for populations with a cure rate, it may be better to use a discrete distribution for the frailty random variable than a continuous distribution. Therefore, we proposed two models in this study. In the first model, continuous gamma as the distribution is used, and in the second model, discrete hyper-Poisson distribution is applied for the frailty random variable. Also, Bayesian inference with Weibull distribution and generalized modified Weibull distribution as the baseline distribution were used in the two proposed models, respectively. In this study, we used data of patients with gastric cancer to show the application of these models in real data analysis. The parameters and regression coefficients were estimated using the Metropolis with Gibbs sampling algorithm, so that this algorithm is one of the crucial techniques in Markov chain Monte Carlo simulation. A simulation study was also used to evaluate the performance of the Bayesian estimates to confirm the proposed models. Based on the results of the Bayesian inference, it was found that the model with generalized modified Weibull and hyper-Poisson distributions is a suitable model in practical study and also this model fits better than the model with Weibull and Gamma distributions.
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Affiliation(s)
- Ali Karamoozian
- Department of Biostatistics and Epidemiology, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran.,Modeling in Health Research Center, Institute for Futures Studies in Health, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran
| | - Mohammad Reza Baneshi
- Department of Biostatistics and Epidemiology, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran.,Modeling in Health Research Center, Institute for Futures Studies in Health, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran
| | - Abbas Bahrampour
- Department of Biostatistics and Epidemiology, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran.,Modeling in Health Research Center, Institute for Futures Studies in Health, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran
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10
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Cancho VG, Suzuki AK, Barriga GDC, Santo APJDE. A multivariate survival model induced by discrete frailty. COMMUN STAT-SIMUL C 2020. [DOI: 10.1080/03610918.2020.1806323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Vicente G. Cancho
- Department of Applied Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| | - Adriano K. Suzuki
- Department of Applied Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
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11
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Wang Y, Tang Y, Zhang J. Bayesian approach for proportional hazards mixture cure model allowing non-curable competing risk. J STAT COMPUT SIM 2019. [DOI: 10.1080/00949655.2019.1695798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Yijun Wang
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
| | - Yincai Tang
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
| | - Jiajia Zhang
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
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12
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Cancho VG, Barriga G, Leão J, Saulo H. Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point. COMMUN STAT-THEOR M 2019. [DOI: 10.1080/03610926.2019.1648826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Vicente G. Cancho
- Department of Mathematics and Statistics, Universidade de São Paulo, São Carlos, Brazil
| | - Gladys Barriga
- Department of Producing Engineering, Universidade Estadual Paulista Júlio de Mesquita Filho, São Paulo, Brazil
| | - Jeremias Leão
- Department of Statistics, Universidade Federal Do Amazonas, Manaus, Brazil
| | - Helton Saulo
- Department of Statistics, Universidade de Brasília, Brasília, Brazil
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13
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A Bayesian Cure Rate Model Based on the Power Piecewise Exponential Distribution. Methodol Comput Appl Probab 2019. [DOI: 10.1007/s11009-019-09728-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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14
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Calsavara VF, Rodrigues AS, Rocha R, Louzada F, Tomazella V, Souza ACRLA, Costa RA, Francisco RPV. Zero-adjusted defective regression models for modeling lifetime data. J Appl Stat 2019. [DOI: 10.1080/02664763.2019.1597029] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Vinicius F. Calsavara
- Department of Epidemiology and Statistics, A.C.Camargo Cancer Center, São Paulo, SP, Brazil
| | - Agatha S. Rodrigues
- Institute of Mathematics and Statistics, University of São Paulo, São Paulo, SP, Brazil
- Department of Obstetrics and Gynecology, São Paulo University Medical School, São Paulo, SP, Brazil
| | - Ricardo Rocha
- Department of Statistics, Federal University of Bahia, Salvador, BA, Brazil
| | - Francisco Louzada
- Institute of Mathematical Science and Computing, University of São Paulo, São Carlos, SP, Brazil
| | - Vera Tomazella
- Department of Statistics, Federal University of São Carlos, São Carlos, SP, Brazil
| | - Ana C. R. L. A. Souza
- Department of Obstetrics and Gynecology, São Paulo University Medical School, São Paulo, SP, Brazil
| | - Rafaela A. Costa
- Department of Obstetrics and Gynecology, São Paulo University Medical School, São Paulo, SP, Brazil
| | - Rossana P. V. Francisco
- Department of Obstetrics and Gynecology, São Paulo University Medical School, São Paulo, SP, Brazil
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15
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Calsavara VF, Rodrigues AS, Rocha R, Tomazella V, Louzada F. Defective regression models for cure rate modeling with interval-censored data. Biom J 2019; 61:841-859. [PMID: 30868619 DOI: 10.1002/bimj.201800056] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 10/17/2018] [Accepted: 02/22/2019] [Indexed: 11/11/2022]
Abstract
Regression models in survival analysis are most commonly applied for right-censored survival data. In some situations, the time to the event is not exactly observed, although it is known that the event occurred between two observed times. In practice, the moment of observation is frequently taken as the event occurrence time, and the interval-censored mechanism is ignored. We present a cure rate defective model for interval-censored event-time data. The defective distribution is characterized by a density function whose integration assumes a value less than one when the parameter domain differs from the usual domain. We use the Gompertz and inverse Gaussian defective distributions to model data containing cured elements and estimate parameters using the maximum likelihood estimation procedure. We evaluate the performance of the proposed models using Monte Carlo simulation studies. Practical relevance of the models is illustrated by applying datasets on ovarian cancer recurrence and oral lesions in children after liver transplantation, both of which were derived from studies performed at A.C. Camargo Cancer Center in São Paulo, Brazil.
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Affiliation(s)
- Vinicius F Calsavara
- Department of Epidemiology and Statistics, A.C. Camargo Cancer Center, São Paulo, SP, Brazil
| | - Agatha S Rodrigues
- Institute of Mathematics and Statistics, University of São Paulo, São Paulo, SP, Brazil.,Department of Obstetrics and Gynecology, São Paulo University Medical School, São Paulo, SP, Brazil
| | - Ricardo Rocha
- Department of Statistics, Federal University of Bahia, Salvador, BA, Brazil
| | - Vera Tomazella
- Department of Statistics, Federal University of São Carlos, São Carlos, SP, Brazil
| | - Francisco Louzada
- Institute of Mathematical Science and Computing, University of São Paulo, São Carlos, SP, Brazil
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16
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de Souza D, Cancho VG, Rodrigues J, Balakrishnan N. Bayesian cure rate models induced by frailty in survival analysis. Stat Methods Med Res 2017; 26:2011-2028. [DOI: 10.1177/0962280217708671] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Frailty models provide a convenient way of modeling unobserved dependence and heterogeneity in survival data which, if not accounted for duly, would result incorrect inference. Gamma frailty models are commonly used for this purpose, but alternative continuous distributions are possible as well. However, with cure rate being present in survival data, these continuous distributions may not be appropriate since individuals with long-term survival times encompass zero frailty. So, we propose here a flexible probability distribution induced by a discrete frailty, and then present some special discrete probability distributions. We specifically focus on a special hyper-Poisson distribution and then develop the corresponding Bayesian simulation, influence diagnostics and an application to real dataset by means of intensive Markov chain Monte Carlo algorithm. These illustrate the usefulness of the proposed model as well as the inferential results developed here.
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Affiliation(s)
- Daiane de Souza
- Department of Applied Mathematics and Statistics, University of São Paulo, São Carlos, Brazil
| | - Vicente G Cancho
- Department of Applied Mathematics and Statistics, University of São Paulo, São Carlos, Brazil
| | - Josemar Rodrigues
- Department of Applied Mathematics and Statistics, University of São Paulo, São Carlos, Brazil
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17
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Suzuki AK, Barriga GDC, Louzada F, Cancho VG. A general long-term aging model with different underlying activation mechanisms: Modeling, Bayesian estimation, and case influence diagnostics. COMMUN STAT-THEOR M 2017. [DOI: 10.1080/03610926.2015.1053945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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18
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Yiqi B, Cancho VG, Louzada F. On the Bayesian estimation and influence diagnostics for the Weibull-Negative-Binomial regression model with cure rate under latent failure causes. COMMUN STAT-THEOR M 2017. [DOI: 10.1080/03610926.2015.1019150] [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]
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19
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Tahir MH, Cordeiro GM. Compounding of distributions: a survey and new generalized classes. JOURNAL OF STATISTICAL DISTRIBUTIONS AND APPLICATIONS 2016. [DOI: 10.1186/s40488-016-0052-1] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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20
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Calsavara VF, Rodrigues AS, Tomazella VLD, de Castro M. Frailty models power variance function with cure fraction and latent risk factors negative binomial. COMMUN STAT-THEOR M 2016. [DOI: 10.1080/03610926.2016.1218029] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Vinicius Fernando Calsavara
- Departamento de Epidemiologia e Estatística, Centro Internacional de Pesquisa, A.C. Camargo Cancer Center, São Paulo-SP, Brazil
- Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo-SP, Brazil
| | - Agatha Sacramento Rodrigues
- Departamento de Epidemiologia e Estatística, Centro Internacional de Pesquisa, A.C. Camargo Cancer Center, São Paulo-SP, Brazil
- Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo-SP, Brazil
| | | | - Mário de Castro
- Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos-SP, Brazil
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21
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Li D, Wang X, Dey DK. A flexible cure rate model for spatially correlated survival data based on generalized extreme value distribution and Gaussian process priors. Biom J 2016; 58:1178-97. [PMID: 27225466 DOI: 10.1002/bimj.201500040] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Revised: 11/11/2015] [Accepted: 12/14/2015] [Indexed: 12/22/2022]
Abstract
Our present work proposes a new survival model in a Bayesian context to analyze right-censored survival data for populations with a surviving fraction, assuming that the log failure time follows a generalized extreme value distribution. Many applications require a more flexible modeling of covariate information than a simple linear or parametric form for all covariate effects. It is also necessary to include the spatial variation in the model, since it is sometimes unexplained by the covariates considered in the analysis. Therefore, the nonlinear covariate effects and the spatial effects are incorporated into the systematic component of our model. Gaussian processes (GPs) provide a natural framework for modeling potentially nonlinear relationship and have recently become extremely powerful in nonlinear regression. Our proposed model adopts a semiparametric Bayesian approach by imposing a GP prior on the nonlinear structure of continuous covariate. With the consideration of data availability and computational complexity, the conditionally autoregressive distribution is placed on the region-specific frailties to handle spatial correlation. The flexibility and gains of our proposed model are illustrated through analyses of simulated data examples as well as a dataset involving a colon cancer clinical trial from the state of Iowa.
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Affiliation(s)
- Dan Li
- Department of Mathematical Sciences, University of Cincinnati, 2815 Commons Way, Cincinnati, Ohio 45221-0025, USA
| | - Xia Wang
- Department of Mathematical Sciences, University of Cincinnati, 2815 Commons Way, Cincinnati, Ohio 45221-0025, USA.
| | - Dipak K Dey
- Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, Connecticut 06269-4098, USA
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22
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Rodrigues J, Cordeiro GM, Cancho VG, Balakrishnan N. Relaxed Poisson cure rate models. Biom J 2015; 58:397-415. [PMID: 26686485 DOI: 10.1002/bimj.201500051] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Revised: 08/06/2015] [Accepted: 09/14/2015] [Indexed: 11/06/2022]
Abstract
The purpose of this article is to make the standard promotion cure rate model (Yakovlev and Tsodikov, ) more flexible by assuming that the number of lesions or altered cells after a treatment follows a fractional Poisson distribution (Laskin, ). It is proved that the well-known Mittag-Leffler relaxation function (Berberan-Santos, ) is a simple way to obtain a new cure rate model that is a compromise between the promotion and geometric cure rate models allowing for superdispersion. So, the relaxed cure rate model developed here can be considered as a natural and less restrictive extension of the popular Poisson cure rate model at the cost of an additional parameter, but a competitor to negative-binomial cure rate models (Rodrigues et al., ). Some mathematical properties of a proper relaxed Poisson density are explored. A simulation study and an illustration of the proposed cure rate model from the Bayesian point of view are finally presented.
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Affiliation(s)
- Josemar Rodrigues
- Department of Applied Mathematics and Statistics, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos-SP, Brazil
| | - Gauss M Cordeiro
- Department of Statistics, Universidade Federal de Pernambuco, Recife-PE, Brazil
| | - Vicente G Cancho
- Department of Applied Mathematics and Statistics, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos-SP, Brazil
| | - N Balakrishnan
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
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23
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Cordeiro GM, Cancho VG, Ortega EMM, Barriga GDC. A model with long-term survivors: negative binomial Birnbaum-Saunders. COMMUN STAT-THEOR M 2015. [DOI: 10.1080/03610926.2013.863929] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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24
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Cancho VG, Louzada F, Dey DK, Barriga GD. A New lifetime model for multivariate survival data with a surviving fraction. J STAT COMPUT SIM 2015. [DOI: 10.1080/00949655.2015.1007983] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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25
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Suzuki AK, Cancho VG, Louzada F. The Poisson–Inverse-Gaussian regression model with cure rate: a Bayesian approach and its case influence diagnostics. Stat Pap (Berl) 2014. [DOI: 10.1007/s00362-014-0649-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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26
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Martinez EZ, Achcar JA. Bayesian bivariate generalized Lindley model for survival data with a cure fraction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 117:145-157. [PMID: 25123102 DOI: 10.1016/j.cmpb.2014.07.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2014] [Revised: 07/22/2014] [Accepted: 07/24/2014] [Indexed: 06/03/2023]
Abstract
The cure fraction models have been widely used to analyze survival data in which a proportion of the individuals is not susceptible to the event of interest. In this article, we introduce a bivariate model for survival data with a cure fraction based on the three-parameter generalized Lindley distribution. The joint distribution of the survival times is obtained by using copula functions. We consider three types of copula function models, the Farlie-Gumbel-Morgenstern (FGM), Clayton and Gumbel-Barnett copulas. The model is implemented under a Bayesian framework, where the parameter estimation is based on Markov Chain Monte Carlo (MCMC) techniques. To illustrate the utility of the model, we consider an application to a real data set related to an invasive cervical cancer study.
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Affiliation(s)
- Edson Z Martinez
- Department of Social Medicine, University of São Paulo (USP), Ribeirão Preto School of Medicine, Brazil.
| | - Jorge A Achcar
- Department of Social Medicine, University of São Paulo (USP), Ribeirão Preto School of Medicine, Brazil
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27
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Martinez EZ, Achcar JA, Jácome AAA, Santos JS. Mixture and non-mixture cure fraction models based on the generalized modified Weibull distribution with an application to gastric cancer data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 112:343-355. [PMID: 24008248 DOI: 10.1016/j.cmpb.2013.07.021] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Revised: 07/17/2013] [Accepted: 07/20/2013] [Indexed: 06/02/2023]
Abstract
The cure fraction models are usually used to model lifetime time data with long-term survivors. In the present article, we introduce a Bayesian analysis of the four-parameter generalized modified Weibull (GMW) distribution in presence of cure fraction, censored data and covariates. In order to include the proportion of "cured" patients, mixture and non-mixture formulation models are considered. To demonstrate the ability of using this model in the analysis of real data, we consider an application to data from patients with gastric adenocarcinoma. Inferences are obtained by using MCMC (Markov Chain Monte Carlo) methods.
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Affiliation(s)
- Edson Z Martinez
- Department of Social Medicine, University of São Paulo (USP), Ribeirão Preto School of Medicine, Brazil.
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28
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Louzada F, de Castro M, Tomazella V, Gonzales JF. Modeling categorical covariates for lifetime data in the presence of cure fraction by Bayesian partition structures. J Appl Stat 2013. [DOI: 10.1080/02664763.2013.847067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Cancho VG, Bandyopadhyay D, Louzada F, Yiqi B. The destructive negative binomial cure rate model with a latent activation scheme. STATISTICAL METHODOLOGY 2013; 13:48-68. [PMID: 23585760 PMCID: PMC3622276 DOI: 10.1016/j.stamet.2013.01.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
A new flexible cure rate survival model is developed where the initial number of competing causes of the event of interest (say lesions or altered cells) follow a compound negative binomial (NB) distribution. This model provides a realistic interpretation of the biological mechanism of the event of interest as it models a destructive process of the initial competing risk factors and records only the damaged portion of the original number of risk factors. Besides, it also accounts for the underlying mechanisms that leads to cure through various latent activation schemes. Our method of estimation exploits maximum likelihood (ML) tools. The methodology is illustrated on a real data set on malignant melanoma, and the finite sample behavior of parameter estimates are explored through simulation studies.
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Affiliation(s)
- Vicente G. Cancho
- Instituto de Ciências Matemáticas e de
Computação, Universidade de São Paulo, Brazil
| | | | - Francisco Louzada
- Instituto de Ciências Matemáticas e de
Computação, Universidade de São Paulo, Brazil
| | - Bao Yiqi
- Instituto de Ciências Matemáticas e de
Computação, Universidade de São Paulo, Brazil
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Louzada F, Cancho VG, Roman M, Leite JG. A new long-term lifetime distribution induced by a latent complementary risk framework. J Appl Stat 2012. [DOI: 10.1080/02664763.2012.706264] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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