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2008
The estimation of duration models has been the subject of significant research in econometrics since the late 1970s. Cox (1972) proposed the use of proportional hazard models in biostatistics and they were soon adopted for use in economics. Since Lancaster (1979), it has been recognized among economists that it is important to account for unobserved ...
Jerry A. Hausman, Tiemen M. Woutersen
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The estimation of duration models has been the subject of significant research in econometrics since the late 1970s. Cox (1972) proposed the use of proportional hazard models in biostatistics and they were soon adopted for use in economics. Since Lancaster (1979), it has been recognized among economists that it is important to account for unobserved ...
Jerry A. Hausman, Tiemen M. Woutersen
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2021
We consider several models that describe survival in the presence of observable covariates, these covariates measuring subject heterogeneity. The most general situation can be described by a model with a parameter of high, possibly unbounded, dimension.
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We consider several models that describe survival in the presence of observable covariates, these covariates measuring subject heterogeneity. The most general situation can be described by a model with a parameter of high, possibly unbounded, dimension.
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Survival Analysis with Cox Proportional Hazards Model in Predicting Patient Outcomes
International Conference Electronic Systems, Signal Processing and Computing Technologies [ICESC-]Survival analysis is crucial for understanding the factors that influence patient outcomes across time. The objective is to predict the outcomes of patient survival under various circumstances using the Cox Proportional Hazards Model. The main objectives
Monikapreethi S K +5 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.
O, Rosen, M, Tanner
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Model misspecification in proportional hazards regression
Biometrika, 1995Summary: The proportional hazards model is frequently used to evaluate the effect of treatment on failure time events in randomised clinical trials. Concomitant variables are usually available and may be considered for use in the primary analyses under the assumption that incorporating them may reduce bias or improve efficiency.
Anderson, Garnet L., Fleming, Thomas R.
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Non-proportional hazards models
2021The most general model, described in Chapter 4 covers a very broad spread of possibilities and, in this chapter, we consider some special cases.
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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 ...
Peter Schmidt, Ann Dryden Witte
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Proportional transition hazards models
2011As with competing risks, the most widely used regression model for multistate data assumes a proportional hazards form for the transition hazards of the multistate model. We re-emphasize that the proportional hazards assumption is made for interpretational and technical convenience. As in Chapter 9, we consider n individuals under study with individual
Jan Beyersmann +2 more
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Proportional hazards model with random effects
Statistics in Medicine, 2000We propose a general proportional hazards model with random effects for handling clustered survival data. This generalizes the usual frailty model by allowing a multivariate random effect with arbitrary design matrix in the log relative risk, in a way similar to the modelling of random effects in linear, generalized linear and non-linear mixed models ...
F, Vaida, R, Xu
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Visualizing covariates in proportional hazards model
Statistics in Medicine, 2009AbstractWe present a graphical method called the rank‐hazard plot that visualizes the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0, 1].
Juha, Karvanen, Frank E, Harrell
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