Mixture Hazard Models for Lifetime Data

Louzada-Neto, Francisco; Mazucheli, Josmar; Achcar, Jorge Alberto

doi:10.1002/1521-4036(200201)44:1<3::aid-bimj3>3.3.co;2-4

Cited by 15 publications

(18 citation statements)

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“…The literature is rich and growing rapidly. The book by Ibrahim et al [25], as well as the papers by Tsodikov et al [26], Louzada-Neto [27], Rodrigues et al [28], Ortega et al [29], and Cancho et al [30] are key references, although with practical focuses on biomedical sciences and engineering.…”

Section: Preliminarymentioning

confidence: 99%

A non‐default rate regression model for credit scoring

Barriga

Cancho

Louzada

2015

Appl Stoch Models Bus & Ind

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In this paper, we propose a new non‐default rate survival model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes, which may be responsible for the occurrence of the event of interest, is assumed to follow a geometric distribution, while the time to event is assumed to follow an inverse Weibull distribution. An advantage of our approach is to accommodate all activation mechanisms based on order statistics. We explore the use of maximum likelihood estimation procedure. Simulation studies are performed and experimental results are illustrated based on a real Brazilian bank personal loan portfolio data. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Preliminarymentioning

confidence: 99%

A non‐default rate regression model for credit scoring

Barriga

Cancho

Louzada

2015

Appl Stoch Models Bus & Ind

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“…A mixture of Weibull hazard functions has been used previously in ‘standard’ survival analysis (McLachlan and McGifffin, 1994; Louzado‐Neto et al , 2002; Blackstone et al , 1986).…”

Section: Mixture and Non‐mixture Cure Fraction Models For Relativementioning

confidence: 99%

Estimating the Cure Fraction in Population-Based Cancer Studies by Using Finite Mixture Models

Lambert

Dickman

Weston

et al. 2009

Journal of the Royal Statistical Society Series C: Applied Statistics

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The cure fraction (the proportion of patients who are cured of disease) is of interest to both patients and clinicians and is a useful measure to monitor trends in survival of curable disease. The paper extends the non-mixture and mixture cure fraction models to estimate the proportion cured of disease in population-based cancer studies by incorporating a finite mixture of two Weibull distributions to provide more flexibility in the shape of the estimated relative survival or excess mortality functions. The methods are illustrated by using public use data from England and Wales on survival following diagnosis of cancer of the colon where interest lies in differences between age and deprivation groups. We show that the finite mixture approach leads to improved model fit and estimates of the cure fraction that are closer to the empirical estimates. This is particularly so in the oldest age group where the cure fraction is notably lower. The cure fraction is broadly similar in each deprivation group, but the median survival of the 'uncured' is lower in the more deprived groups. The finite mixture approach overcomes some of the limitations of the more simplistic cure models and has the potential to model the complex excess hazard functions that are seen in real data. Copyright (c) 2010 Royal Statistical Society.

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“…Following the proposition of comprehensive cure rate models [Yakovlev and Tsodikov, 1996], , [Chen et al, 2002], [Chen et al, 1999], [Tsodikov et al, 2003], [Tournoud and Ecochard, 2007], [de Castro et al, 2009], [Ortega et al, 2009], [de Castro et al, 2010], [Cancho et al, 2011b] and [Kim et al, 2011] include in their formulation the possibility of having a cured rate in the population, and assume the occurrence of the event of interest might be a result of many competing causes [Gordon, 1990], but both number of causes and survival times associated with each cause [Cox and Oakes, 1984] being unknown, which leads to the so-called latent competing causes [Louzada-Neto, 1999], which are assumed to follows a discrete distribution such as the Poisson, the negative binomial, the geometric, the COM-Poisson, the power series, amongst others.…”

Section: Introductionmentioning

confidence: 99%

A general long-term aging model with different underlying activation mechanisms: Modeling, Bayesian estimation, and case influence diagnostics

Suzuki

Barriga

Louzada

et al. 2016

Communications in Statistics - Theory and Methods

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In this paper we propose a general cure rate aging model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes of the event of interest is assumed to follow a logarithmic distribution. The model is parametrized in terms of the cured fraction which is then linked to covariates. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis for the proposed model. Moreover, some discussions on the model selection to compare the fitted models are given, as well as, case deletion influence diagnostics are developed for the joint posterior distribution based on the ψ-divergence, which has several divergence measures as particular cases, such as the Kullback-Leibler (K-L), J-distance, L 1 norm and χ 2 -square divergence measures. Simulation studies are performed and experimental results are illustrated based on a real malignant melanoma data.

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Mixture Hazard Models for Lifetime Data

Cited by 15 publications

References 0 publications

A non‐default rate regression model for credit scoring

A non‐default rate regression model for credit scoring

Estimating the Cure Fraction in Population-Based Cancer Studies by Using Finite Mixture Models

A general long-term aging model with different underlying activation mechanisms: Modeling, Bayesian estimation, and case influence diagnostics

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