Bayesian survival analysis with BUGS

Álvares, Danilo; Lázaro, Elena; Gómez‐Rubio, Virgilio; Pardo, Leandro

doi:10.1002/sim.8933

Cited by 30 publications

(21 citation statements)

References 80 publications

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“…A good place to find them is on the page of Pezzullo and his collaborators 23 . Bugs, 24 R 25 and SAS 26 include different procedures or packages for more complex analyses. Klein and Zhang 27 compared the performances of BMDP, SAS, SPSS, S‐PLUS and STATA to perform basic calculations for censored data.…”

Section: Methods Of Estimationmentioning

confidence: 99%

Study design synopsis: Survive the race – Time‐to‐event analysis

Detilleux

2021

Equine Veterinary Journal

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Section: Methods Of Estimationmentioning

confidence: 99%

Study design synopsis: Survive the race – Time‐to‐event analysis

Detilleux

2021

Equine Veterinary Journal

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“…The baseline hazard can be chosen to be the hazard associated to the 3-parameter Power Generalised Weibull or Generalised Gamma distributions which can capture the basic shapes of interest in practice (increasing, decreasing, unimodal, and bathtub). [23] Simpler 2-parameter distributions such as the Log-Normal, Log-Logistic, or Gamma distributions can be used as well. In our implementation, we allow for a variety of combinations of baseline hazards.…”

Section: Methodological Frameworkmentioning

confidence: 99%

Bayesian variable selection and survival modeling: Assessing the most important comorbidities that impact lung and colorectal cancer survival in Spain

Rubio

Álvares

Redondo‐Sánchez

et al. 2021

Preprint

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Cancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by a number of factors, such as comorbidities, that may interact with the cancer tumour. Moreover, it is of interest to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to a worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities in the overall survival in cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages mombf and rstan. We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer.

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“…The prior distribution is elicited assuming a prior independence and non-informative scenario. Wide normal prior distributions were selected for the regression coefficients, β, and Gamma distributions for the shape and scale parameters α and λ (See Alvares et al, (2021) for a wide tutorial on Bayesian survival models). Posterior distributions were approximated via Markov Chain Monte Carlo methods (MCMC) as well as through the integrated nested Laplace approximation (INLA).…”

Section: Illness-death Modelmentioning

confidence: 99%

Reflection on modern methods: competing risks versus multi-state models

Llopis-Cardona,

Armero,

Sanfélix-Gimeno

2021

Preprint

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Survival competing risks models are very useful for studying the incidence of diseases whose occurrence competes with other possible diseases or health conditions. These models perform properly when working with terminal events, such as death, that imply the conclusion of the corresponding study. But they do not allow the treatment of scenarios with non-terminal competing events that may occur sequentially. Multi-state models are complex survival models. They focus on pathways defined by the temporal and sequential occurrence of numerous events of interest and thus they are suitable for connecting competing non-terminal events as well as to manage other survival scenarios with higher complexity. We discuss competing risks within the framework of multi-state models and clarify the usefulness of both models for analysing epidemiological data. We highlight the power of multi-state models through a real-world study of recurrent hip fracture from Bayesian inferential methodology.

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Bayesian survival analysis with BUGS

Cited by 30 publications

References 80 publications

Study design synopsis: Survive the race – Time‐to‐event analysis

Study design synopsis: Survive the race – Time‐to‐event analysis

Bayesian variable selection and survival modeling: Assessing the most important comorbidities that impact lung and colorectal cancer survival in Spain

Reflection on modern methods: competing risks versus multi-state models

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