Results 241 to 250 of about 377,632 (286)

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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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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Regression Dilution in the Proportional Hazards Model

Biometrics, 1993
The 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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Survival Analysis Cox’s Proportional Hazards Model)

2001
Abstract The San Francisco Men’s Health Study is based on a sample of 1034 single men ages 24 to 54 years. These men were recruited using a multistage prob­ ability sample and were followed from July 1984 to December 1987. Mem­ bers of the cohort were interviewed and examined every six months.
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A hybrid repair-replacement policy in the proportional hazards model

European Journal of Operational Research, 2023
Rui Zheng
exaly