Bayesian mixed model for survival data with semicompeting risks based on the Clayton copula

被引：0

作者：

Patino, Elizabeth Gonzalez ^{[1
]}

Tunes, Gisela ^{[2
]}

Tanaka, Nelson Ithiro ^{[2
]}

机构：

[1] Inst Butantan, Clin Trials & Pharmacovigilance Ctr, Sao Paulo, Brazil

[2] Univ Sao Paulo, Stat Dept, Sao Paulo, Brazil

来源：

BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS | 2024年 / 38卷 / 02期

关键词：

Semicompeting risks; Archimedean copulas; Clayton copula; data augumentation; chronic kidney disease; mixed model; SEMI-COMPETING RISKS; FRAILTY MODELS; DISTRIBUTIONS;

D O I：

10.1214/24-BJPS606

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Motivated by a chronic kidney disease dataset, we propose a Bayesian model for clustered semicompeting risks data based on Archimedean copulas, allowing for treatment switching. We consider the modeling of both independent and clustered observations. For clustered data, random effects are included to consider the dependence among observations in the same group. For the Clayton copula, we provide theoretical results for the posterior distribution when improper priors are used. A simulation study was conducted to evaluate the performance of the proposed model. Finally, the results of the analysis of chronic kidney disease data are discussed.

引用

页码：302 / 320

页数：19

共 50 条

[41] Uncertainty evaluation in atomic force microscopy measurement of nanoparticles based on statistical mixed model in a Bayesian framework [J].

Petry, J. ;

Boeck, B. De ;

Sebaihi, N. ;

Coenegrachts, M. ;

Caebergs, T. ;

Dobre, M. .

MEASUREMENT SCIENCE AND TECHNOLOGY, 2021, 32 (08)

[42] Marginalized Frailty-Based Illness-Death Model: Application to the UK-Biobank Survival Data [J].

Gorfine, Malka ;

Keret, Nir ;

Ben Arie, Asaf ;

Zucker, David ;

Hsu, Li .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2021, 116 (535) :1155-1167

[43] Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model [J].

Wang, Jue ;

Luo, Sheng .

STATISTICAL METHODS IN MEDICAL RESEARCH, 2019, 28 (10-11) :3392-3403

[44] A MIXED-EFFECTS JOINT MODEL WITH SKEW-T DISTRIBUTION FOR LONGITUDINAL AND TIME-TO-EVENT DATA: A BAYESIAN APPROACH [J].

Ferede, Melkamu M. ;

Mwalili, Samuel M. ;

Dagne, Getachew A. .

COMMUNICATIONS IN MATHEMATICAL BIOLOGY AND NEUROSCIENCE, 2023,

[45] COST-OFFSETS OF PRESCRIPTION DRUG EXPENDITURES: DATA ANALYSIS VIA A COPULA-BASED BIVARIATE DYNAMIC HURDLE MODEL [J].

Deb, Partha ;

Trivedi, Pravin K. ;

Zimmer, David M. .

HEALTH ECONOMICS, 2014, 23 (10) :1242-1259

[46] Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features [J].

Zhang, Hanze ;

Huang, Yangxin ;

Wang, Wei ;

Chen, Henian ;

Langland-Orban, Barbara .

STATISTICAL METHODS IN MEDICAL RESEARCH, 2019, 28 (02) :569-588

[47] Quantile regression-based Bayesian joint modeling analysis of longitudinal-survival data, with application to an AIDS cohort study [J].

Zhang, Hanze ;

Huang, Yangxin .

LIFETIME DATA ANALYSIS, 2020, 26 (02) :339-368

[48] Bayesian and non-Bayesian inference for inverse Weibull model based on jointly type-II hybrid censoring samples with modeling to physics data [J].

Al Mutairi, Aned ;

Khashab, Rana H. ;

Almetwally, Ehab M. ;

Abo-Kasem, O. E. ;

Ibrahim, Gamal M. .

AIP ADVANCES, 2023, 13 (10)

[49] Bayesian Inference for Mixed Model-Based Genome-Wide Analysis of Expression Quantitative Trait Loci by Gibbs Sampling [J].

Lee, Chaeyoung .

FRONTIERS IN GENETICS, 2019, 10

[50] E-Bayesian Prediction for the Burr XII Model Based on Type-II Censored Data with Two Samples [J].

Okasha, Hassan M. ;

Wang, Chuanmei ;

Wang, Jianhua .

ADVANCES IN MATHEMATICAL PHYSICS, 2020, 2020

← 1 2 3 4 5 →