scholarly journals A competing risks model with time‐varying heterogeneity and simultaneous failure

2020 ◽  
Vol 11 (2) ◽  
pp. 535-577 ◽  
Author(s):  
Ruixuan Liu
Keyword(s):  
Competing Risks ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Estimation Procedure ◽  
Time Varying ◽  
Competing Risks Model ◽  
Hazards Model ◽  
Competing Risks Data ◽  
Passage Times ◽  
Lévy Subordinators

This paper proposes a new bivariate competing risks model in which both durations are the first passage times of dependent Lévy subordinators with exponential thresholds and multiplicative covariates effects. Our specification extends the mixed proportional hazards model, as it allows for the time‐varying heterogeneity represented by the unobservable Lévy processes and it generates the simultaneous termination of both durations with positive probability. We obtain nonparametric identification of all model primitives given competing risks data. A flexible semiparametric estimation procedure is provided and illustrated through the analysis of a real dataset.

Analysing multicentre competing risks data with a mixed proportional hazards model for the subdistribution

2006 ◽  
Vol 25 (24) ◽  
pp. 4267-4278 ◽  
Author(s):  
Sandrine Katsahian ◽  
Matthieu Resche-Rigon ◽  
Sylvie Chevret ◽  
Raphaël Porcher
Keyword(s):  
Competing Risks ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Hazards Model ◽  
Competing Risks Data ◽  
Mixed Proportional Hazards

The Identifiability of the Mixed Proportional Hazards Model with Time-Varying Coefficients

1996 ◽  
Vol 12 (4) ◽  
pp. 733-738 ◽  
Author(s):  
Brian P. McCall
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Time Varying ◽  
Varying Coefficients ◽  
Hazards Model ◽  
Time Varying Coefficients ◽  
Mixed Proportional Hazards

This paper establishes conditions for the nonparametric identifiability of the mixed proportional hazards model with time-varying coefficients. Unlike the mixed proportional hazards model, a regressor with two distinct values is not sufficient to identify this model. An unbounded regressor, however, is sufficient for identification.

scholarly journals ANALYSIS AND APPLICATIONS OF THE RESIDUAL VARENTROPY OF RANDOM LIFETIMES

2020 ◽  
pp. 1-19 ◽  
Author(s):  
Antonio Di Crescenzo ◽  
Luca Paolillo
Keyword(s):  
Information Content ◽  
Stochastic Systems ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Jump Diffusion ◽  
Reliability Theory ◽  
Residual Entropy ◽  
Dynamic Information ◽  
Hazards Model ◽  
Passage Times

Abstract Reliability theory and survival analysis, the residual entropy is known as a measure suitable to describe the dynamic information content in stochastic systems conditional on survival. Aiming to analyze the variability of such information content, in this paper we introduce the variance of the residual lifetimes, “residual varentropy” in short. After a theoretical investigation of some properties of the residual varentropy, we illustrate certain applications related to the proportional hazards model and the first-passage times of an Ornstein–Uhlenbeck jump-diffusion process.

scholarly journals Competing risks’ analysis in cardiovascular disease epidemiology; – Τhe case of ATTICA study (2002-2012)

2020 ◽  
pp. 19-25
Author(s):  
Thomas Tsiampalis ◽  
Demosthenes Panagiotakos
Keyword(s):  
Cardiovascular Disease ◽  
Competing Risks ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Survival Function ◽  
Gray Model ◽  
Disease Epidemiology ◽  
Hazards Model ◽  
One To One

Background: In studies of all-cause mortality, a one-to-one relation connects the hazard with the survival and as a consequence the regression models which focus on the hazard, such as the proportional hazards model, immediately dictate how the covariates relate to the survival function, as well. However, these two concepts and their one-to-one relation are totally different in the context of competing risks, where the terms of cause-specific hazard and cumulative incidence function appear. Objective: The aim of the present work was to present two of the most popular methods (cause-specific hazard model and Fine & Gray model) through an application on cardiovascular disease epidemiology (CVD), as well as, to narratively review more recent publications, based on either the frequentist, or the Bayesian approach to inference. Methods: A narrative review of the most widely used methods in the competing risks setting was conducted, extended to more recent publications. For the application, our interest lied in modeling the risk of Coronary Heart Disease in the presence of vascular stroke, by using the cause-specific hazard and the Fine & Gray models, two of most commonly encountered approaches. Results-Conclusions: After the implementation of these two approaches in the context of competing risks in CVD epidemiology, it is noted that while the use of the Fine & Gray model includes information about the existence of a competing risk, the interpretation of the results is not as easy as in the case of the cause-specific risk Cox model.

scholarly journals Moving beyond the Cox proportional hazards model in survival data analysis: a cervical cancer study

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e033965
Author(s):  
Lixian Li ◽  
Zijing Yang ◽  
Yawen Hou ◽  
Zheng Chen
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Figo Stage ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Time Varying ◽  
Node Metastasis ◽  
Hazards Model

ObjectivesThis study explored the prognostic factors and developed a prediction model for Chinese-American (CA) cervical cancer (CC) patients. We compared two alternative models (the restricted mean survival time (RMST) model and the proportional baselines landmark supermodel (PBLS model, producing dynamic prediction)) versus the Cox proportional hazards model in the context of time-varying effects.Setting and data sourcesA total of 713 CA women with CC and available covariates (age at diagnosis, International Federation of Gynecology and Obstetrics (FIGO) stage, lymph node metastasis and radiation) from the Surveillance, Epidemiology and End Results database were included.DesignWe applied the Cox proportional hazards model to analyse the all-cause mortality with the proportional hazards assumption. Additionally, we applied two alternative models to analyse covariates with time-varying effects. The performances of the models were compared using the C-index for discrimination and the shrinkage slope for calibration.ResultsOlder patients had a worse survival rate than younger patients. Advanced FIGO stage patients showed a relatively poor survival rate and low life expectancy. Lymph node metastasis was an unfavourable prognostic factor in our models. Age at diagnosis, FIGO stage and lymph node metastasis represented time-varying effects from the PBLS model. Additionally, radiation showed no impact on survival in any model. Dynamic prediction presented a better performance for 5-year dynamic death rates than did the Cox proportional hazards model.ConclusionsWith the time-varying effects, the RMST model was suggested to explore diagnosis factors, and the PBLS model was recommended to predict a patient’s w-year dynamic death rate.

Subdistribution hazard models for competing risks in discrete time

Biostatistics ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 449-466 ◽  
Author(s):  
Moritz Berger ◽  
Matthias Schmid ◽  
Thomas Welchowski ◽  
Steffen Schmitz-Valckenberg ◽  
Jan Beyersmann
Keyword(s):  
Competing Risks ◽  
Discrete Time ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Model Parameters ◽  
Subdistribution Hazard ◽  
Ml Estimation ◽  
Modeling Approach ◽  
Hazards Model ◽  
Event Times

Summary A popular modeling approach for competing risks analysis in longitudinal studies is the proportional subdistribution hazards model by Fine and Gray (1999. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association94, 496–509). This model is widely used for the analysis of continuous event times in clinical and epidemiological studies. However, it does not apply when event times are measured on a discrete time scale, which is a likely scenario when events occur between pairs of consecutive points in time (e.g., between two follow-up visits of an epidemiological study) and when the exact lengths of the continuous time spans are not known. To adapt the Fine and Gray approach to this situation, we propose a technique for modeling subdistribution hazards in discrete time. Our method, which results in consistent and asymptotically normal estimators of the model parameters, is based on a weighted ML estimation scheme for binary regression. We illustrate the modeling approach by an analysis of nosocomial pneumonia in patients treated in hospitals.

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