Semiparametric analysis of clustered interval-censored survival data using soft Bayesian Additive Regression Trees (SBART).

@article{Basak2021SemiparametricAO,
  title={Semiparametric analysis of clustered interval-censored survival data using soft Bayesian Additive Regression Trees (SBART).},
  author={Piyali Basak and Antonio R. Linero and Debajyoti Sinha and Stuart R. Lipsitz},
  journal={Biometrics},
  year={2021}
}
Popular parametric and semiparametric hazards regression models for clustered survival data are inappropriate and inadequate when the unknown effects of different covariates and clustering are complex. This calls for a flexible modeling framework to yield efficient survival prediction. Moreover, for some survival studies involving time to occurrence of some asymptomatic events, survival times are typically interval censored between consecutive clinical inspections. In this article, we propose a… 

Figures and Tables from this paper

Bayesian Survival Tree Ensembles with Submodel Shrinkage
TLDR
This work considers Bayesian nonparametric estimation of a survival time subject to right-censoring in the presence of potentially high-dimensional predictors and proposes two models based on the Bayesian additive regression trees (BART) framework, including a Bayesian implementation of Cox’s partial likelihood.
Bayesian Additive Regression Trees for Multivariate Responses
  • S. Um
  • Computer Science
  • 2022
TLDR
This dissertation introduces a nonparametric regression approach for multivariate skewed responses using Bayesian additive regression trees (BART), and provides a useful extension of BART, called the skewBART model, to address this problem.
The how and why of Bayesian nonparametric causal inference
TLDR
A comprehensive overview of Bayesian nonparametric applications to causal inference is presented and it is argued that most of the time it is necessary to model both the selection and outcome processes.
Adaptive Conditional Distribution Estimation with Bayesian Decision Tree Ensembles
TLDR
A Bayesian nonparametric model for conditional distribution estimation using Bayesian additive regression trees (BART) is presented and an approach to targeted smoothing is introduced which is possibly of independent interest for BART models.
Bayesian Additive Regression Trees for Genotype by Environment Interaction Models
TLDR
This work proposes a new class of models for the estimation of Genotype by Environment (GxE) interactions in plant-based genetics using semi-parametric Bayesian Additive Regression Trees to accurately capture marginal genotypic and environment effects along with their interaction in a fully Bayesian model.

References

SHOWING 1-10 OF 49 REFERENCES
Nonparametric survival analysis using Bayesian Additive Regression Trees (BART)
TLDR
Modelling that extends the usefulness of BART in medical applications by addressing needs arising in survival analysis by demonstrating the model's ability to accommodate data from complex regression models with a simulation study of a nonproportional hazards scenario.
A semiparametric Bayesian proportional hazards model for interval censored data with frailty effects
TLDR
The proposed software supports the solution of complex analyses in many fields of clinical epidemiology as well as health services research.
Boosting proportional hazards models using smoothing splines, with applications to high-dimensional microarray data
TLDR
Results from predicting survival after chemotherapy for patients with diffuse large B-cell lymphoma demonstrate that the proposed boosting procedure using smoothing splines for estimating the general proportional hazards models can indeed recover the true functional forms of the covariates and can identify important variables that are related to the risk of an event.
Gaussian Processes for Survival Analysis
TLDR
A semi-parametric Bayesian model for survival analysis that handles left, right and interval censoring mechanisms common in survival analysis, and proposes a MCMC algorithm to perform inference and an approximation scheme based on random Fourier features to make computations faster.
Semiparametric mixed‐scale models using shared Bayesian forests
TLDR
This paper presents a methodology which allows for information to be shared nonparametrically across various model components using Bayesian sum‐of‐tree models, and demonstrates the advantages of sharing information about unknown features of covariates across multiple model components in various nonparametric regression problems.
Random survival forests
TLDR
This article introduces random survival forests, a random forests method for the analysis of right-censored survival data, and extends Breiman’s random forests (RF) method, showing it to be highly accurate and comparable to state-of-the-art methods.
Modeling interval-censored, clustered cow udder quarter infection times through the shared gamma frailty model
Time to infection data are often simultaneously clustered and interval-censored. The time to infection is not known exactly; it is only known to have occurred within a certain interval. Moreover,
Frailty models for survival data
A frailty model is a random effects model for time variables, where the random effect (the frailty) has a multiplicative effect on the hazard. It can be used for univariate (independent) failure
Log-Linear Bayesian Additive Regression Trees for Categorical and Count Responses
TLDR
This paper introduces Bayesian additive regression trees (BART) for log-linear models including multinomial logistic regression and count regression with zero-inflation and overdispersion and develops new data augmentation strategies and carefully specified prior distributions for these new models.
A Model for Association in Bivariate Survival Data
SUMMARY A reparameterization of a model introduced by D. G. Clayton for association in bivariate life-tables is discussed. Inference for the parameter governing the association is con- sidered when
...
...