Bayesian analysis of a disability model for lung cancer survival

Pardo, Leandro; Cabras, Stefano; Castellanos, María Eugenia; Perra, Silvia; Quirós, Alicia; Oruezábal, M; Ferrández, Javier Sánchez-Rubio

doi:10.1177/0962280212452803

Cited by 18 publications

(23 citation statements)

References 36 publications

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“…In order to have tractable models, several assumptions have been made either restricting the parametric families of the sojourn times or permitting only progressive state transitions. Kang and Lagakos () assume that the transition intensities from at least one of the states need to be time‐homogeneous while Armero et al () discuss a specific progressive disability model with Weibull sojourn distributions. Titman and Sharples () focus on the tractable class of semi‐Markov models with phase‐type sojourn distributions.…”

Section: Introductionmentioning

confidence: 99%

Approximate Bayesian Inference for Discretely Observed Continuous-Time Multi-State Models

Tancredi

2019

Biometrics

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Inference for continuous time multi-state models presents considerable computational difficulties when the process is only observed at discrete time points with no additional information about the state transitions. In fact, for general multi-state Markov model, evaluation of the likelihood function is possible only via intensive numerical approximations. Moreover, in real applications, transitions between states may depend on the time since entry into the current state, and semi-Markov models, where the likelihood function is not available in closed form, should be fitted to the data. Approximate Bayesian Computation (ABC) methods, which make use only of comparisons between simulated and observed summary statistics, represent a solution to intractable likelihood problems and provide alternative algorithms when the likelihood calculation is computationally too costly. In this article we investigate the potentiality of ABC techniques for multi-state models both to obtain the posterior distributions of the model parameters and to compare Markov and semi-Markov models. In addition, we will also exploit ABC methods to estimate and compare hidden Markov and semi-Markov models when observed states are subject to classification errors. We illustrate the performance of the ABC methodology both with simulated data and with a real data example.

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Section: Introductionmentioning

confidence: 99%

Approximate Bayesian Inference for Discretely Observed Continuous-Time Multi-State Models

Tancredi

2019

Biometrics

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“…While analysts have many options for handling these phenomena, to the best of our knowledge few papers have considered them simultaneously and most of these have been in the context of the Cox model for the hazard function; Web Appendix A provides a comprehensive overview of the literature and existing software options. In regard to AFT models for semi‐competing risks data, while a number of relevant papers have been published (Ding et al, ; Ghosh et al, ; Armero et al, ; Jiang and Haneuse, ), each of them is limited in their application to the ACT study, since they do not accommodate left‐truncation or interval‐censoring. Motivated by this, we propose a flexible, robust Bayesian framework for the analysis of an AFT model for semi‐competing risks data subject to left‐truncation and/or interval‐censoring.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Failure Time Models for Semi-Competing Risks Data in the Presence of Complex Censoring

2017

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Statistical analyses that investigate risk factors for Alzheimer's disease (AD) are often subject to a number of challenges. Some of these challenges arise due to practical considerations regarding data collection such that the observation of AD events is subject to complex censoring including left-truncation and either interval or right-censoring. Additional challenges arise due to the fact that study participants under investigation are often subject to competing forces, most notably death, that may not be independent of AD. Towards resolving the latter, researchers may choose to embed the study of AD within the "semi-competing risks" framework for which the recent statistical literature has seen a number of advances including for the so-called illness-death model. To the best of our knowledge, however, the semi-competing risks literature has not fully considered analyses in contexts with complex censoring, as in studies of AD. This is particularly the case when interest lies with the accelerated failure time (AFT) model, an alternative to the traditional multiplicative Cox model that places emphasis away from the hazard function. In this article, we outline a new Bayesian framework for estimation/inference of an AFT illness-death model for semi-competing risks data subject to complex censoring. An efficient computational algorithm that gives researchers the flexibility to adopt either a fully parametric or a semi-parametric model specification is developed and implemented. The proposed methods are motivated by and illustrated with an analysis of data from the Adult Changes in Thought study, an on-going community-based prospective study of incident AD in western Washington State.

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“…Armero et al. () present a Weibull semi‐Markov model for the three states disability model (see Section ). Kim, James, and Weissbach () develop a semi‐parametric regression model based on a Markov process and a beta‐Dirichlet process for the cumulative intensity functions.…”

Section: Introductionmentioning

confidence: 99%

Bayesian path specific frailty models for multi‐state survival data with applications

2015

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Summary Multi-state models can be viewed as generalizations of both the standard and competing risks models for survival data. Models for multi-state data have been the theme of many recent published works. Motivated by bone marrow transplant data, we propose a Bayesian model using the gap times between two successive events in a path of events experienced by a subject. Path specific frailties are introduced to capture the dependence structure of the gap times in the paths with two or more states. Under improper prior distributions for the parameters, we establish propriety of the posterior distribution. An efficient Gibbs sampling algorithm is developed for drawing samples from the posterior distribution. An extensive simulation study is carried out to examine the empirical performance of the proposed approach. A bone marrow transplant data set is analyzed in detail to further demonstrate the proposed methodology.

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Bayesian analysis of a disability model for lung cancer survival

Cited by 18 publications

References 36 publications

Approximate Bayesian Inference for Discretely Observed Continuous-Time Multi-State Models

Approximate Bayesian Inference for Discretely Observed Continuous-Time Multi-State Models

Accelerated Failure Time Models for Semi-Competing Risks Data in the Presence of Complex Censoring

Bayesian path specific frailty models for multi‐state survival data with applications

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