The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform
Jan Beyersmann - One of the best experts on this subject based on the ideXlab platform.
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Understanding competing risks: a simulation point of view
BMC medical research methodology, 2011Co-Authors: Arthur Allignol, Martin Schumacher, Christoph Wanner, Christiane Drechsler, Jan BeyersmannAbstract:Competing risks methodology allows for an event-specific analysis of the single components of composite time-to-event endpoints. A key feature of competing risks is that there are as many hazards as there are competing risks. This is not always well accounted for in the applied literature. We advocate a simulation point of view for understanding competing risks. The hazards are envisaged as momentary event forces. They jointly determine the event time. Their relative magnitude determines the event type. 'Empirical simulations' using data from a recent study on cardiovascular events in diabetes patients illustrate subsequent interpretation. The method avoids concerns on identifiability and plausibility known from the Latent Failure time approach. The 'empirical simulations' served as a proof of concept. Additionally manipulating baseline hazards and treatment effects illustrated both scenarios that require greater care for interpretation and how the simulation point of view aids the interpretation. The simulation algorithm applied to real data also provides for a general tool for study planning. There are as many hazards as there are competing risks. All of them should be analysed. This includes estimation of baseline hazards. Study planning must equally account for these aspects.
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Understanding competing risks: a simulation point of view
BMC Medical Research Methodology, 2011Co-Authors: Arthur Allignol, Martin Schumacher, Christoph Wanner, Christiane Drechsler, Jan BeyersmannAbstract:Background Competing risks methodology allows for an event-specific analysis of the single components of composite time-to-event endpoints. A key feature of competing risks is that there are as many hazards as there are competing risks. This is not always well accounted for in the applied literature. Methods We advocate a simulation point of view for understanding competing risks. The hazards are envisaged as momentary event forces. They jointly determine the event time. Their relative magnitude determines the event type. 'Empirical simulations' using data from a recent study on cardiovascular events in diabetes patients illustrate subsequent interpretation. The method avoids concerns on identifiability and plausibility known from the Latent Failure time approach. Results The 'empirical simulations' served as a proof of concept. Additionally manipulating baseline hazards and treatment effects illustrated both scenarios that require greater care for interpretation and how the simulation point of view aids the interpretation. The simulation algorithm applied to real data also provides for a general tool for study planning. Conclusions There are as many hazards as there are competing risks. All of them should be analysed. This includes estimation of baseline hazards. Study planning must equally account for these aspects.
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Simulating competing risks data in survival analysis.
Statistics in medicine, 2009Co-Authors: Jan Beyersmann, Aurelien Latouche, Anika Buchholz, Martin SchumacherAbstract:Competing risks analysis considers time-to-first-event ('survival time') and the event type ('cause'), possibly subject to right-censoring. The cause-, i.e. event-specific hazards, completely determine the competing risk process, but simulation studies often fall back on the much criticized Latent Failure time model. Cause-specific hazard-driven simulation appears to be the exception; if done, usually only constant hazards are considered, which will be unrealistic in many medical situations. We explain simulating competing risks data based on possibly time-dependent cause-specific hazards. The simulation design is as easy as any other, relies on identifiable quantities only and adds to our understanding of the competing risks process. In addition, it immediately generalizes to more complex multistate models. We apply the proposed simulation design to computing the least false parameter of a misspecified proportional subdistribution hazard model, which is a research question of independent interest in competing risks. The simulation specifications have been motivated by data on infectious complications in stem-cell transplanted patients, where results from cause-specific hazards analyses were difficult to interpret in terms of cumulative event probabilities. The simulation illustrates that results from a misspecified proportional subdistribution hazard analysis can be interpreted as a time-averaged effect on the cumulative event probability scale.
Martin Schumacher - One of the best experts on this subject based on the ideXlab platform.
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Understanding competing risks: a simulation point of view
BMC medical research methodology, 2011Co-Authors: Arthur Allignol, Martin Schumacher, Christoph Wanner, Christiane Drechsler, Jan BeyersmannAbstract:Competing risks methodology allows for an event-specific analysis of the single components of composite time-to-event endpoints. A key feature of competing risks is that there are as many hazards as there are competing risks. This is not always well accounted for in the applied literature. We advocate a simulation point of view for understanding competing risks. The hazards are envisaged as momentary event forces. They jointly determine the event time. Their relative magnitude determines the event type. 'Empirical simulations' using data from a recent study on cardiovascular events in diabetes patients illustrate subsequent interpretation. The method avoids concerns on identifiability and plausibility known from the Latent Failure time approach. The 'empirical simulations' served as a proof of concept. Additionally manipulating baseline hazards and treatment effects illustrated both scenarios that require greater care for interpretation and how the simulation point of view aids the interpretation. The simulation algorithm applied to real data also provides for a general tool for study planning. There are as many hazards as there are competing risks. All of them should be analysed. This includes estimation of baseline hazards. Study planning must equally account for these aspects.
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Understanding competing risks: a simulation point of view
BMC Medical Research Methodology, 2011Co-Authors: Arthur Allignol, Martin Schumacher, Christoph Wanner, Christiane Drechsler, Jan BeyersmannAbstract:Background Competing risks methodology allows for an event-specific analysis of the single components of composite time-to-event endpoints. A key feature of competing risks is that there are as many hazards as there are competing risks. This is not always well accounted for in the applied literature. Methods We advocate a simulation point of view for understanding competing risks. The hazards are envisaged as momentary event forces. They jointly determine the event time. Their relative magnitude determines the event type. 'Empirical simulations' using data from a recent study on cardiovascular events in diabetes patients illustrate subsequent interpretation. The method avoids concerns on identifiability and plausibility known from the Latent Failure time approach. Results The 'empirical simulations' served as a proof of concept. Additionally manipulating baseline hazards and treatment effects illustrated both scenarios that require greater care for interpretation and how the simulation point of view aids the interpretation. The simulation algorithm applied to real data also provides for a general tool for study planning. Conclusions There are as many hazards as there are competing risks. All of them should be analysed. This includes estimation of baseline hazards. Study planning must equally account for these aspects.
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Simulating competing risks data in survival analysis.
Statistics in medicine, 2009Co-Authors: Jan Beyersmann, Aurelien Latouche, Anika Buchholz, Martin SchumacherAbstract:Competing risks analysis considers time-to-first-event ('survival time') and the event type ('cause'), possibly subject to right-censoring. The cause-, i.e. event-specific hazards, completely determine the competing risk process, but simulation studies often fall back on the much criticized Latent Failure time model. Cause-specific hazard-driven simulation appears to be the exception; if done, usually only constant hazards are considered, which will be unrealistic in many medical situations. We explain simulating competing risks data based on possibly time-dependent cause-specific hazards. The simulation design is as easy as any other, relies on identifiable quantities only and adds to our understanding of the competing risks process. In addition, it immediately generalizes to more complex multistate models. We apply the proposed simulation design to computing the least false parameter of a misspecified proportional subdistribution hazard model, which is a research question of independent interest in competing risks. The simulation specifications have been motivated by data on infectious complications in stem-cell transplanted patients, where results from cause-specific hazards analyses were difficult to interpret in terms of cumulative event probabilities. The simulation illustrates that results from a misspecified proportional subdistribution hazard analysis can be interpreted as a time-averaged effect on the cumulative event probability scale.
Arthur Allignol - One of the best experts on this subject based on the ideXlab platform.
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Understanding competing risks: a simulation point of view
BMC medical research methodology, 2011Co-Authors: Arthur Allignol, Martin Schumacher, Christoph Wanner, Christiane Drechsler, Jan BeyersmannAbstract:Competing risks methodology allows for an event-specific analysis of the single components of composite time-to-event endpoints. A key feature of competing risks is that there are as many hazards as there are competing risks. This is not always well accounted for in the applied literature. We advocate a simulation point of view for understanding competing risks. The hazards are envisaged as momentary event forces. They jointly determine the event time. Their relative magnitude determines the event type. 'Empirical simulations' using data from a recent study on cardiovascular events in diabetes patients illustrate subsequent interpretation. The method avoids concerns on identifiability and plausibility known from the Latent Failure time approach. The 'empirical simulations' served as a proof of concept. Additionally manipulating baseline hazards and treatment effects illustrated both scenarios that require greater care for interpretation and how the simulation point of view aids the interpretation. The simulation algorithm applied to real data also provides for a general tool for study planning. There are as many hazards as there are competing risks. All of them should be analysed. This includes estimation of baseline hazards. Study planning must equally account for these aspects.
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Understanding competing risks: a simulation point of view
BMC Medical Research Methodology, 2011Co-Authors: Arthur Allignol, Martin Schumacher, Christoph Wanner, Christiane Drechsler, Jan BeyersmannAbstract:Background Competing risks methodology allows for an event-specific analysis of the single components of composite time-to-event endpoints. A key feature of competing risks is that there are as many hazards as there are competing risks. This is not always well accounted for in the applied literature. Methods We advocate a simulation point of view for understanding competing risks. The hazards are envisaged as momentary event forces. They jointly determine the event time. Their relative magnitude determines the event type. 'Empirical simulations' using data from a recent study on cardiovascular events in diabetes patients illustrate subsequent interpretation. The method avoids concerns on identifiability and plausibility known from the Latent Failure time approach. Results The 'empirical simulations' served as a proof of concept. Additionally manipulating baseline hazards and treatment effects illustrated both scenarios that require greater care for interpretation and how the simulation point of view aids the interpretation. The simulation algorithm applied to real data also provides for a general tool for study planning. Conclusions There are as many hazards as there are competing risks. All of them should be analysed. This includes estimation of baseline hazards. Study planning must equally account for these aspects.
Francisco Louzada - One of the best experts on this subject based on the ideXlab platform.
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on the bayesian estimation and influence diagnostics for the weibull negative binomial regression model with cure rate under Latent Failure causes
Communications in Statistics-theory and Methods, 2017Co-Authors: Bao Yiqi, Vicente G. Cancho, Francisco LouzadaAbstract:ABSTRACTThe purpose of this paper is to develop a Bayesian approach for the Weibull-Negative-Binomial regression model with cure rate under Latent Failure causes and presence of randomized activation mechanisms. We assume the number of competing causes of the event of interest follows a Negative Binomial (NB) distribution while the Latent lifetimes are assumed to follow a Weibull distribution. Markov chain Monte Carlos (MCMC) methods are used to develop the Bayesian procedure. Model selection to compare the fitted models is discussed. Moreover, we develop case deletion influence diagnostics for the joint posterior distribution based on the ψ-divergence, which has several divergence measures as particular cases. The developed procedures are illustrated with a real data set.
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influence diagnostics for the weibull negative binomial regression model with cure rate under Latent Failure causes
Journal of Applied Statistics, 2015Co-Authors: Bao Yiqi, Vicente G. Cancho, Cibele M Russo, Francisco LouzadaAbstract:In this paper, we propose a flexible cure rate survival model by assuming that the number of competing causes of the event of interest follows the Negative Binomial distribution and the time to event follows a Weibull distribution. Indeed, we introduce the Weibull-Negative-Binomial (WNB) distribution, which can be used in order to model survival data when the hazard rate function is increasing, decreasing and some non-monotonous shaped. Another advantage of the proposed model is that it has some distributions commonly used in lifetime analysis as particular cases. Moreover, the proposed model includes as special cases some of the well-know cure rate models discussed in the literature. We consider a frequentist analysis for parameter estimation of a WNB model with cure rate. Then, we derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some ways to perform global influence analysis. Finally, the methodology is illustrated on a medical data.
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Influence diagnostics for the Weibull-Negative-Binomial regression model with cure rate under Latent Failure causes
Journal of Applied Statistics, 2015Co-Authors: Bao Yiqi, Vicente G. Cancho, Cibele M Russo, Francisco LouzadaAbstract:In this paper, we propose a flexible cure rate survival model by assuming that the number of competing causes of the event of interest follows the Negative Binomial distribution and the time to event follows a Weibull distribution. Indeed, we introduce the Weibull-Negative-Binomial (WNB) distribution, which can be used in order to model survival data when the hazard rate function is increasing, decreasing and some non-monotonous shaped. Another advantage of the proposed model is that it has some distributions commonly used in lifetime analysis as particular cases. Moreover, the proposed model includes as special cases some of the well-know cure rate models discussed in the literature. We consider a frequentist analysis for parameter estimation of a WNB model with cure rate. Then, we derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some ways to perform global influence analysis. Finally, the methodology is illustrated ...
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The log-Weibull-negative-binomial regression model under Latent Failure causes and presence of randomized activation schemes
Statistics, 2014Co-Authors: Francisco Louzada, Vicente G. Cancho, Bao YiqiAbstract:The purpose of this paper is to develop a Bayesian approach for the log-Weibull-negative-binomial regression model under Latent Failure causes and presence of a randomized activation mechanism. We assume the number of competing causes of the event of interest follows a negative binomial distribution while the Latent lifetimes are assumed to follows a Weibull distribution. Markov chain Monte Carlo methods are used to develop a Bayesian approach. Model selection to compare the fitted models is discussed. Moreover, we develop case deletion influence diagnostics for the joint posterior distribution based on the ψ-divergence, which has several divergence measures as particular cases. The developed procedures are illustrated on artificial and real data sets.
Aurelien Latouche - One of the best experts on this subject based on the ideXlab platform.
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Simulating competing risks data in survival analysis.
Statistics in medicine, 2009Co-Authors: Jan Beyersmann, Aurelien Latouche, Anika Buchholz, Martin SchumacherAbstract:Competing risks analysis considers time-to-first-event ('survival time') and the event type ('cause'), possibly subject to right-censoring. The cause-, i.e. event-specific hazards, completely determine the competing risk process, but simulation studies often fall back on the much criticized Latent Failure time model. Cause-specific hazard-driven simulation appears to be the exception; if done, usually only constant hazards are considered, which will be unrealistic in many medical situations. We explain simulating competing risks data based on possibly time-dependent cause-specific hazards. The simulation design is as easy as any other, relies on identifiable quantities only and adds to our understanding of the competing risks process. In addition, it immediately generalizes to more complex multistate models. We apply the proposed simulation design to computing the least false parameter of a misspecified proportional subdistribution hazard model, which is a research question of independent interest in competing risks. The simulation specifications have been motivated by data on infectious complications in stem-cell transplanted patients, where results from cause-specific hazards analyses were difficult to interpret in terms of cumulative event probabilities. The simulation illustrates that results from a misspecified proportional subdistribution hazard analysis can be interpreted as a time-averaged effect on the cumulative event probability scale.
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misspecified regression model for the subdistribution hazard of a competing risk
Statistics in Medicine, 2007Co-Authors: Aurelien Latouche, V Boisson, Sylvie Chevret, Raphael PorcherAbstract:We consider a competing risks setting, when evaluating the prognostic influence of an exposure on a specific cause of Failure. Two main regression models are used in such analyses, the Cox cause-specific proportional hazards model and the subdistribution proportional hazards model. They are exemplified in a real data example focusing on relapse-free interval in acute leukaemia patients. We examine the properties of the estimator based on the latter model when the true model is the former. An explicit relationship between subdistribution hazards ratio and cause-specific hazards ratio is derived, assuming a flexible parametric distribution for Latent Failure times.
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Misspecified regression model for the subdistribution hazard of a competing risk.
Stat Med, 2007Co-Authors: Aurelien Latouche, V Boisson, Sylvie Chevret, Raphael PorcherAbstract:We consider a competing risks setting, when evaluating the prognostic influence of an exposure on a specific cause of Failure. Two main regression models are used in such analyses, the Cox cause-specific proportional hazards model and the subdistribution proportional hazards model. They are exemplified in a real data example focusing on relapse-free interval in acute leukaemia patients. We examine the properties of the estimator based on the latter model when the true model is the former. An explicit relationship between subdistribution hazards ratio and cause-specific hazards ratio is derived, assuming a flexible parametric distribution for Latent Failure times. Copyright (c) 2006 John Wiley & Sons, Ltd.
