Bayesian analysis for monotone hazard ratio

Kim, Yongdai; Park, Jin Kyung; Kim, Gwangsu

doi:10.1007/s10985-010-9181-x

Cited by 8 publications

(9 citation statements)

References 18 publications

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“…See, for example, Dimatteo et al (2001), Hennerfeind (2006), Sharef et al (2010), Xing et al (2012) and Nasri et al (2013). In addition, a prior based on a Gamma process is devised for monotonically increasing time-varying coefficients, which have been frequently used as an alternative to constant coefficients (Gill & Schumacher, 1987;Deshpande & Sengupta, 1995;Kim et al, 2011). Moreover, monotonically increasing time-varying coefficients are used to assess the existence of waning of treatment effect (Durham et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of the Proportional Hazards Model with Time‐Varying Coefficients

Kim

Choi

2017

Scandinavian J Statistics

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We study a Bayesian analysis of the proportional hazards model with time‐varying coefficients. We consider two priors for time‐varying coefficients – one based on B‐spline basis functions and the other based on Gamma processes – and we use a beta process prior for the baseline hazard functions. We show that the two priors provide optimal posterior convergence rates (up to the normallogn term) and that the Bayes factor is consistent for testing the assumption of the proportional hazards when the two priors are used for an alternative hypothesis. In addition, adaptive priors are considered for theoretical investigation, in which the smoothness of the true function is assumed to be unknown, and prior distributions are assigned based on B‐splines.

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Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of the Proportional Hazards Model with Time‐Varying Coefficients

Kim

Choi

2017

Scandinavian J Statistics

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“…In this study, we follow the common practice to select d jk , j = 1, … , G − 1 as the

\frac{j}{G}

th percentile of X ik and conduct sensitivity analysis by disturbing G from 5 to 10. Kim et al 29 and others also considered alternative approaches, such as the Bayesian Bootstrap prior, for modeling the baseline hazard function.…”

Section: Bayesian Inferencementioning

confidence: 99%

Joint analysis of mixed types of outcomes with latent variables

Pan

Wei

Song

2020

Statistics in Medicine

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We propose a joint modeling approach to investigating the observed and latent risk factors of mixed types of outcomes. The proposed model comprises three parts. The first part is an exploratory factor analysis model that summarizes latent factors through multiple observed variables. The second part is a proportional hazards model that examines the observed and latent risk factors of multivariate time‐to‐event outcomes. The third part is a linear regression model that investigates the determinants of a continuous outcome. We develop a Bayesian approach coupled with MCMC methods to determine the number of latent factors, the association between latent and observed variables, and the important risk factors of different types of outcomes. A modified stochastic search item selection algorithm, which introduces normal‐mixture‐inverse gamma priors to factor loadings and regression coefficients, is developed for simultaneous model selection and parameter estimation. The proposed method is subjected to simulation studies for empirical performance assessment and then applied to a study concerning the risk factors of type 2 diabetes and the associated complications.

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“…Details of the Bayesian bootstrap and applications can be found in Kim and Lee ( 2003 ) and Kim et al. ( 2011 ). After obtaining the posterior samples, we calculated the Bayes estimates of If the estimates are over 0.5, we reject equivalence of hazard functions, i.e., we choose a model with higher posterior probability.…”

Section: Partial Likelihood Priors and Posteriors For The Testmentioning

confidence: 99%

“…Although Kalbfleisch ( 1978 ), Hjort ( 1990 ), and Kim ( 2006 ) turned to the Bayesian methodology for estimation of hazard or survival function and Kim et al. ( 2011 ) proposed the Bayesian test for monotone hazards, to our best knowledge, there are only a few studies done for testing equivalence of hazards including crossing hazards.…”

Section: Introductionmentioning

confidence: 99%

Bayesian test for hazard ratio in survival analysis

Kim

Lee

2016

SpringerPlus

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Over the decades, testing for equivalence of hazard functions has received a wide attention in survival analysis. In this paper, we proposed a Bayesian test to address this testing equivalence problem, Most of all, proposed test is methodologically flexible so that a procedure determining weights is not required when the proportional assumption is violated. In comparison with popularly exploited methods, the proposed test is shown to be more powerful and robust in testing differences of hazard functions, in spite of the presence of crossing hazard functions. Extensive applications to simulation and real data were conducted, demonstrating that the proposed test presents outstanding performance and hold desirable properties in terms of numerical aspects.

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Bayesian analysis for monotone hazard ratio

Cited by 8 publications

References 18 publications

Bayesian Analysis of the Proportional Hazards Model with Time‐Varying Coefficients

Bayesian Analysis of the Proportional Hazards Model with Time‐Varying Coefficients

Joint analysis of mixed types of outcomes with latent variables

Bayesian test for hazard ratio in survival analysis

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