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.

Proportionally Difficult: Testing for Nonproportional Hazards in Cox Models

2010 ◽  
Vol 18 (2) ◽  
pp. 189-205 ◽  
Author(s):  
Luke Keele
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Standard Test ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Cox Models ◽  
Nonproportional Hazards ◽  
Specification Errors ◽  
Hazards Model

The Cox proportional hazards model is widely used to model durations in the social sciences. Although this model allows analysts to forgo choices about the form of the hazard, it demands careful attention to the proportional hazards assumption. To this end, a standard diagnostic method has been developed to test this assumption. I argue that the standard test for nonproportional hazards has been misunderstood in current practice. This test detects a variety of specification errors, and these specification errors must be corrected before one can correctly diagnose nonproportionality. In particular, unmodeled nonlinearity can appear as a violation of the proportional hazard assumption for the Cox model. Using both simulation and empirical examples, I demonstrate how an analyst might be led astray by incorrectly applying the nonproportionality test.

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.

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.

scholarly journals Reduced rank proportional hazards model for competing risks

Biostatistics ◽  
2005 ◽  
Vol 6 (3) ◽  
pp. 465-478 ◽  
Author(s):  
M. Fiocco ◽  
H. Putter ◽  
J. C. van Houwelingen
Keyword(s):  
Competing Risks ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Reduced Rank ◽  
Hazards Model

scholarly journals Combining Symbolic Regression with the Cox Proportional Hazards Model Improves Prediction of Heart Failure Deaths

2021 ◽  
Author(s):  
Casper Wilstrup ◽  
Chris Cave
Keyword(s):  
Heart Failure ◽  
Medical Records ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Symbolic Regression ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Data Set ◽  
Hazards Model

Abstract Background: Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone. Methods: We used a newly invented symbolic regression method called the QLattice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified a minimal set of mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.Results: An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations. Conclusion: Symbolic regression is a way to find transformations of covariates from patients’ medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.

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