Performance of Bayesian Using Conjugate Prior Estimator for Weibull Right Censored Survival Data

Thamrin, Sri Astuti; Zoraida, Masli Nurcahya; Jaya, Andi Kresna; Ansariadi, Ansariadi

doi:10.3923/ajsr.2018.376.382

Cited by 4 publications

(8 citation statements)

References 5 publications

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“…A number of studies have used Bayesian survival models to investigate the hospitalisation for dengue in Indonesia [11][12][13][14]. Two studies used a Weibull spatial survival model with a CAR frailty introduced by Banerjee et al [11,14].…”

Section: Introductionmentioning

confidence: 99%

“…However, these papers implemented only one form of spatial prior (a CAR prior) and focused only on important factors associated with dengue. Other authors used a Bayesian Weibull survival model for dengue patients in Makassar, Indonesia, but this study did not consider including a spatial term in their model [12,13]. None of these papers appeared to examine how well the predictive values from the model fit the observed data which is critical for estimation and prediction.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

Aswi

Cramb

Duncan

et al. 2020

IJERPH

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Spatial models are becoming more popular in time-to-event data analysis. Commonly, the intrinsic conditional autoregressive prior is placed on an area level frailty term to allow for correlation between areas. We considered a range of Bayesian Weibull and Cox semiparametric spatial models to describe a dataset on hospitalisation of dengue. This paper aimed to extend these two models, to evaluate the suitability of these models for estimation and prediction of the length of stay, compare different spatial priors, and determine factors that significantly affect the duration of hospital stay for dengue fever patients in the case study location, namely Wahidin hospital in Makassar, Indonesia. We compared two different models with three different spatial priors with respect to goodness of fit and generalisability. For all models considered, the Leroux prior was preferred over the intrinsic conditional autoregressive and independent priors, but Cox and Weibull versions had similar predictive performance, model fit, and results. Age and platelet count were negatively associated with the length of stay, while red blood cell count was positively associated with the length of stay of dengue patients at this hospital. Using appropriate Bayesian spatial survival models enables identification of factors that substantively affect the length of stay.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

Aswi

Cramb

Duncan

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…The Bayes method is one parameter estimation method that can be used for this purpose. The Bayes method combines the likelihood function and the prior distribution of the parameters to be estimated, so that the posterior distribution is obtained which will be the basis of the parameter estimation (Thamrin et al, 2018).…”

Section: Basic Concepts In Bayesian Methodsmentioning

confidence: 99%

“…In using Bayes method, most of researchers used the noninformative prior as the prior distribution, those are uniform prior or Jeffreys' prior. Meanwhile many studies suggested to use the conjugate prior in order to obtain better estimated values (Thamrin et al, 2018), (Corrales and Cepeda-Cuervo, 2019). No work has been done in comparing between conjugate prior and non-informative prior yet.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Two Priors in Bayesian Estimation for Parameter of Weibull Distribution

Yanuar¹,

Yozza²,

Rescha³

2019

sci. technol. indones.

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This present study purposes to conduct Bayesian inference to estimate scale parameters, denoted by θ, with known location parameter or β, of Weibull distribution. There are two types of prior distributions used in this study, conjugate prior and non informative prior. As conjugate prior is inverse gamma, and as non-informative prior is Jeffreys' prior. This research also aims to study several theoretical properties of posterior distribution based on prior used implement it to generated data and make comparison between both Bayes estimator as well. The method used to evaluate as the best estimator is based on the smallest Mean Square Error (MSE) value. This study proveds that Bayes estimator using conjugate prior produces better estimated parameter value estimate non-informative prior since it produces smaller MSE value, for condition θ > 1 based on analytic and simulation study. Meanwhile for θ < 1 both priors could not yield acceptable estimated parameter value.

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“…Reference [24] indicated that one of the ways we could analyze survival time function was to estimate the value of the distribution parameters. He was silent on the method to adopt.…”

Section: The One Parameter Shape Parametermentioning

confidence: 99%

Cross-comparative Analysis of Frequentist and Bayesian Perspectives on Failure Time Distributions

Turkson

2022

EJMS

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In reliability analysis, both the Weibull and the lognormal distributions could be analyzed using the observed data logarithms. While the Weibull data logarithm is skewed, the lognormal data logarithm is symmetrical. This review work initiates discussions on and syntheses of the various views held on the use of the frequentist and Bayesian approaches to drawing statistical inferences on failure time distributions of survival models. Of greater concern was the discussion on the use of the exponential, Weibull and log-normal distributions in reliability analysis. Various methods have been used to discriminate between the two most important distributions. They include: Coefficients of variation (CV); the standard deviation of the data logarithms (SD); the percentile position of the mean of the data logarithm (PP); the cumulated logarithm dispersion before and after the mean (CLD); ratio of the maximum likelihood (RML); Kullback-Leibler Divergence (KLD); and Minimized Kullback-Leibler Divergence (RMKLD). In the (CV, SD, PP and CLD) study, a stress-strength data set was used for the analysis. The stress data followed a lognormal distribution, while the strength data followed a Weibull distribution, therefore for the stress-strength analysis the lognormal-Weibull combination was used. In the ratio of the maximum likelihood (RML) study, it was averred that though each of the distributions had great applications, none of them produced a good fit. In the study using Kullback-Leibler Divergence (KLD); and Minimized Kullback-Leibler Divergence (RMKLD), test results revealed that RML=0.345>0. Hence the lognormal distribution was selected. Similarly, the RMKLD=0.6028>0, therefore the Weibull distribution was selected. In the final analysis, none of the study results could reject the Weibull in favour of the lognormal distribution model. In respect of the frequentist and the Bayesian approach to conducting statistical inferences, it came out strongly that it was high time psychologist who had adopted the use of the frequentist framework moved away from it and started using the Bayesian approach. A shift towards the use of the Bayesian, lognormal-Weibull approach is thus recommendable.

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Performance of Bayesian Using Conjugate Prior Estimator for Weibull Right Censored Survival Data

Cited by 4 publications

References 5 publications

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

Comparison of Two Priors in Bayesian Estimation for Parameter of Weibull Distribution

Cross-comparative Analysis of Frequentist and Bayesian Perspectives on Failure Time Distributions

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