A unified framework for fitting Bayesian semiparametric models to arbitrarily censored survival data, including spatially referenced data
ABSTRACT A comprehensive, unified approach to modeling arbitrarily censored spatial
survival data is presented for the three most commonly used semiparametric models …
survival data is presented for the three most commonly used semiparametric models …
spBayesSurv: Fitting Bayesian spatial survival models using R
Spatial survival analysis has received a great deal of attention over the last 20 years due to
the important role that geographical information can play in predicting survival. This paper …
the important role that geographical information can play in predicting survival. This paper …
Bayesian spatial survival models for hospitalisation of Dengue: A case study of Wahidin hospital in Makassar, Indonesia
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 …
intrinsic conditional autoregressive prior is placed on an area level frailty term to allow for …
Amoud class for hazard-based and odds-based regression models: Application to oncology studies
The purpose of this study is to propose a novel, general, tractable, fully parametric class for
hazard-based and odds-based models of survival regression for the analysis of censored …
hazard-based and odds-based models of survival regression for the analysis of censored …
Bayesian and Frequentist Approaches for a Tractable Parametric General Class of Hazard-Based Regression Models: An Application to Oncology Data
In this study, we consider a general, flexible, parametric hazard-based regression model for
censored lifetime data with covariates and term it the “general hazard (GH)” regression …
censored lifetime data with covariates and term it the “general hazard (GH)” regression …
Spatial survival modelling of business re-opening after Katrina: Survival modelling compared to spatial probit modelling of re-opening within 3, 6 or 12 months
RS Bivand, V Gómez-Rubio - Statistical Modelling, 2021 - journals.sagepub.com
Zhou and Hanson; Zhou and Hanson; Zhou and Hanson (, Nonparametric Bayesian
Inference in Biostatistics, pages 215–46. Cham: Springer; 2018, Journal of the American …
Inference in Biostatistics, pages 215–46. Cham: Springer; 2018, Journal of the American …
[HTML][HTML] A Spatial Variation Analysis of In-Hospital Stroke Mortality Based on Integrated Pre-Hospital and Hospital Data in Mashhad, Iran
Background: Despite significant advances in the quality and delivery of specialized stroke
care, there still persist remarkable spatial variations in emergency medical services (EMS) …
care, there still persist remarkable spatial variations in emergency medical services (EMS) …
Bayesian Variable Selection in Double Generalized Linear Tweedie Spatial Process Models
Double generalized linear models provide a flexible framework for modeling data by
allowing the mean and the dispersion to vary across observations. Common members of the …
allowing the mean and the dispersion to vary across observations. Common members of the …
[PDF][PDF] The accelerated failure time regression model under the extended-exponential distribution with survival analysis
The accelerated failure time regression model under the extended-exponential distribution with
survival analysis Page 1 http://www.aimspress.com/journal/Math AIMS Mathematics, 9(6) …
survival analysis Page 1 http://www.aimspress.com/journal/Math AIMS Mathematics, 9(6) …
Bayesian nonparametric biostatistics
WO Johnson, M De Carvalho - Nonparametric Bayesian Inference in …, 2015 - Springer
We discuss some typical applications of Bayesian nonparametrics in biostatistics. The
chosen applications highlight how Bayesian nonparametrics can contribute to addressing …
chosen applications highlight how Bayesian nonparametrics can contribute to addressing …