A Bayesian semiparametric accelerate failure time mixture cure model

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yijun Wang ◽  
Weiwei Wang ◽  
Yincai Tang
Keyword(s):  
Survival Data ◽  
Dirichlet Process ◽  
Failure Time ◽  
Parametric Method ◽  
Clinical Trial Data ◽  
Estimation Of Parameters ◽  
Slice Sampling ◽  
Mixture Cure Model ◽  
Semiparametric Method ◽  
Bayesian Semiparametric

Abstract The accelerated failure time mixture cure (AFTMC) model is widely used for survival data when a portion of patients can be cured. In this paper, a Bayesian semiparametric method is proposed to obtain the estimation of parameters and density distribution for both the cure probability and the survival distribution of the uncured patients in the AFTMC model. Specifically, the baseline error distribution of the uncured patients is nonparametrically modeled by a mixture of Dirichlet process. Based on the stick-breaking formulation of the Dirichlet process, the techniques of retrospective and slice sampling, an efficient and easy-to-implement Gibbs sampler is developed for the posterior calculation. The proposed approach can be easily implemented in commonly used statistical softwares, and its performance is comparable to fully parametric method via comprehensive simulation studies. Besides, the proposed approach is adopted to the analysis of a colorectal cancer clinical trial data.

Bayesian semiparametric failure time models for multivariate censored data with latent variables

2018 ◽  
Vol 37 (28) ◽  
pp. 4279-4297 ◽  
Author(s):  
Ming Ouyang ◽  
Xiaoqing Wang ◽  
Chunjie Wang ◽  
Xinyuan Song
Keyword(s):  
Censored Data ◽  
Latent Variables ◽  
Failure Time ◽  
Bayesian Semiparametric

scholarly journals Additive Hazard Regression for the Analysis of Clustered Survival Data from Case-Cohort Studies

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
June Liu ◽  
Yi Zhang
Keyword(s):  
Cohort Studies ◽  
Survival Data ◽  
Failure Time ◽  
Weight Functions ◽  
Disease Rate ◽  
Failure Time Data ◽  
Finite Sample ◽  
Time Data ◽  
Cohort Design ◽  
Clustered Survival Data

The case-cohort design is an effective and economical method in large cohort studies, especially when the disease rate is low. Case-cohort design in most of the existing literature is mainly used to analyze the univariate failure time data. But in practice, multivariate failure time data are commonly encountered in biomedical research. In this paper, we will propose methods based on estimating equation method for case-cohort designs for clustered survival data. By introducing the event failure rate, three different weight functions are constructed. Then, three estimating equations and parameter estimators are presented. Furthermore, consistency and asymptotic normality of the proposed estimators are established. Finally, the simulation results show that the proposed estimation procedure has reasonable finite sample behaviors.

A proportional risk model for time-to-event analysis in randomized controlled trials

2020 ◽  
pp. 096228022095359
Author(s):  
Oliver Kuss ◽  
Annika Hoyer
Keyword(s):  
Proportional Hazards Model ◽  
Failure Time ◽  
Risk Model ◽  
Likelihood Function ◽  
Controlled Trial ◽  
Parametric Method ◽  
Parametric Models ◽  
Time To Event ◽  
Technical Problems ◽  
Randomized Controlled

Regression models for continuous, binary, nominal, and ordinal outcomes almost completely rely on parametric models, whereas time-to-event outcomes are mainly analyzed by Cox’s Proportional Hazards model, an essentially non-parametric method. This is done despite a long list of disadvantages that have been reported for the hazard ratio, and also for the odds ratio, another effect measure sometimes used for time-to-event modelling. In this paper, we propose a parametric proportional risk model for time-to-event outcomes in a two-group situation. Modelling explicitly a risk instead of a hazard or an odds solves the current interpretational and technical problems of the latter two effect measures. The model further allows for computing absolute effect measures like risk differences or numbers needed to treat. As an additional benefit, results from the model can also be communicated on the original time scale, as an accelerated or a prolongated failure time thus facilitating interpretation for a non-technical audience. Parameter estimation by maximum likelihood, while properly accounting for censoring, is straightforward and can be implemented in each statistical package that allows coding and maximizing a univariate likelihood function. We illustrate the model with an example from a randomized controlled trial on efficacy of a new glucose-lowering drug for the treatment of type 2 diabetes mellitus and give the results of a small simulation study.

A Poisson approach to the validation of failure time surrogate endpoints in individual patient data meta-analyses

2017 ◽  
Vol 28 (1) ◽  
pp. 170-183 ◽  
Author(s):  
Federico Rotolo ◽  
Xavier Paoletti ◽  
Tomasz Burzykowski ◽  
Marc Buyse ◽  
Stefan Michiels
Keyword(s):  
Survival Data ◽  
Randomized Trials ◽  
Failure Time ◽  
Individual Patient Data ◽  
Random Effect ◽  
Patient Data ◽  
Surrogate Endpoints ◽  
Operating Characteristics ◽  
Individual Level ◽  
Meta Analyses

Surrogate endpoints are often used in clinical trials instead of well-established hard endpoints for practical convenience. The meta-analytic approach relies on two measures of surrogacy: one at the individual level and one at the trial level. In the survival data setting, a two-step model based on copulas is commonly used. We present a new approach which employs a bivariate survival model with an individual random effect shared between the two endpoints and correlated treatment-by-trial interactions. We fit this model using auxiliary mixed Poisson models. We study via simulations the operating characteristics of this mixed Poisson approach as compared to the two-step copula approach. We illustrate the application of the methods on two individual patient data meta-analyses in gastric cancer, in the advanced setting (4069 patients from 20 randomized trials) and in the adjuvant setting (3288 patients from 14 randomized trials).

Sign in / Sign up

Export Citation Format

Share Document