Results 231 to 240 of about 786,239 (293)

Proportional transition hazards models

2011
As 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, Martin Schumacher, Arthur Allignol   +2 more
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Proportional hazards model with random effects

Statistics in Medicine, 2000
We 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, 2009
AbstractWe 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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Conditional Proportional Hazards Models

1996
Bivariate survival models can sometimes be characterized in terms of conditional survival functions of the form P(X > x|Y > y) and P(Y > y|X > x). Attention is focussed on models in which these conditional survival functions are of the proportional hazards form.
Barry C. Arnold, Yong Hee Kim
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Frailty models that yield proportional hazards

Statistics & Probability Letters, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Aalen, Odd O., Hjort, Nils Lid
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A hybrid repair-replacement policy in the proportional hazards model

European Journal of Operational Research, 2022
Rui Zheng, Jingjing Wang, Yingzhi Zhang
semanticscholar   +1 more source

Discrete Proportional Hazards Models for Mismeasured Outcomes

Biometrics, 2003
Outcome mismeasurement can lead to biased estimation in several contexts. Magder and Hughes (1997, American Journal of Epidemiology 146, 195-203) showed that failure to adjust for imperfect outcome measures in logistic regression analysis can conservatively bias estimation of covariate effects, even when the mismeasurement rate is the same across ...
Meier, Amalia S., Richardson, Barbra A., Hughes, James P.   +2 more
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Masking Unmasked in the Proportional Hazards Model

Biometrics, 2000
Summary.Influence measures based on the pairwise deletion approach and the differentiation approach are developed for unmasking observations masked by other observations in the proportional hazards model. These influential observations might have substantial impact on statistical inference and might provide important information for model adequacy. One
Wei, Wen Hsiang, Kosorok, Michael R.
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Reduced-rank hazard regression for modelling non-proportional hazards

Statistics in Medicine, 2006
The Cox proportional hazards model is the most common method to analyse survival data. However, the proportional hazards assumption might not hold. The natural extension of the Cox model is to introduce time-varying effects of the covariates. For some covariates such as (surgical)treatment non-proportionality could be expected beforehand.
Perperoglou, Aris, Cessie, Saskia le, van Houwelingen, Hans C   +2 more
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Condition-based maintenance with dynamic thresholds for a system using the proportional hazards model

Reliability Engineering & System Safety, 2020
Rui Zheng, Bingkun Chen, Liudong Gu
semanticscholar   +1 more source