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2011
This chapter discusses the most widely used regression models in competing risks. Following an introduction in Section 5.1, Section 5.2 discusses proportional cause-specific hazards models, and Section 5.3 discusses the proportional subdistribution hazards model. The cause-specific hazards are as defined in Chapter 3.
Martin Schumacher +2 more
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This chapter discusses the most widely used regression models in competing risks. Following an introduction in Section 5.1, Section 5.2 discusses proportional cause-specific hazards models, and Section 5.3 discusses the proportional subdistribution hazards model. The cause-specific hazards are as defined in Chapter 3.
Martin Schumacher +2 more
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The Proportional Hazards Model
1988In this chapter and Chapter 7, we will consider models of the length of time until recidivism that contain individual characteristics as explanatory variables. The models of Chapter 7 will be parametric models in the sense that they will assume a particular distribution for the survival times; for example, we will estimate a model based on the ...
Ann Dryden Witte, Peter Schmidt
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The proportional hazards model in reliability
Proceedings., Annual Reliability and Maintainability Symposium, 2003The regression model for survival analysis introduced by D.R. Cox (1972) was developed with applications to industrial reliability studies and medical studies in mind. While this model has had a significant impact on the biomedical field, it has received little attention in the reliability literature.
R.V. Spring +2 more
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Regression analysis of bivariate current status data under the proportional hazards model
, 2017This article discusses the regression analysis of bivariate current status or case I interval‐censored failure time data under the marginal proportional hazards model.
T. Hu, Qingning Zhou, Jianguo Sun
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Proportional Hazards Models of Graduation
Journal of College Student Retention: Research, Theory & Practice, 2007Survival analysis is a statistical tool used to describe the duration between events. Many processes in medical research, engineering, and economics can be described using survival analysis techniques. This research involves studying engineering college student graduation using Cox proportional hazards models. Among male students with American College
Justin R. Chimka +2 more
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Mixtures of proportional hazards regression models
Statistics in Medicine, 1999This paper presents a mixture model which combines features of the usual Cox proportional hazards model with those of a class of models, known as mixtures-of-experts. The resulting model is more flexible than the usual Cox model in the sense that the log hazard ratio is allowed to vary non-linearly as a function of the covariates.
Ori Rosen +2 more
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Proportional hazards model: a review [PDF]
Reliability Engineering & System Safety, 1994 Abstract The proportional hazards model was introduced in 1972 by D. R. Cox in order to estimate the effects of different covariates influencing the times to the failures of a system. The model has been used rather extensively in biomedicine and, recently, interest in its application in reliability engineering has increased.
Dhananjay Kumar, Bengt Klefsjö
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Proportional hazards models in epidemiology
2021The basic questions of epidemiology are reconsidered in this chapter from the standpoint of a survival model. We rework the calculations of relative risk, where the time factor is now age, and we see how our survival models can be used to control for the effects of age.
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Estimation in generalized proportional hazards model
Comptes Rendus de l'Académie des Sciences - Series I - Mathematics, 1998Summary: Generalization of the proportional hazards model taking into account dependence of the rate of resource using on the value of the used resource is considered. A modified partial likelihood approach for parameter estimation is proposed. The asymptotic properties of estimators are investigated.
Mikhail Nikulin +1 more
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Regression Dilution in the Proportional Hazards Model
Biometrics, 1993The problem of regression dilution arising from covariate measurement error is investigated for survival data using the proportional hazards model. The naive approach to parameter estimation is considered whereby observed covariate values are used, inappropriately, in the usual analysis instead of the underlying covariate values. A relationship between
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