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Cox proportional hazards regression model. [PDF]
, 2014 Cox proportional hazards regression model (n = 309 episodes in 1117 patients).Cox proportional hazards regression model.
Juan Wang (115708) +10 more
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Flexible Boosting of Accelerated Failure Time Models [PDF]
, 2008 When boosting algorithms are used for building survival models from high-dimensional data, it is common to fit a Cox proportional hazards model or to apply semiparametric least squares techniques.
Torsten Hothorn +5 more
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Trend-constrained corrected score for proportional hazards model with covariate measurement error
Contemporary Clinical Trials Communications, 2015 In many medical research studies, survival time is typically the primary outcome of interest. The Cox proportional hazards model is the most popular method to investigate the relationship between covariates and possibly right-censored survival time ...
Ming Zhu, Yijian Huang
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Cox proportional hazards model results. [PDF]
, 2018 Cox proportional hazards model results.
Donald R. Nelson (5040641) +1 more
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Multivariate Cox proportional hazards model. [PDF]
, 2016 Multivariate Cox proportional hazards model.
Wojciech Surtel (3325326) +5 more
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Proportional hazards models with continuous marks
The Annals of Statistics, 2009 For time-to-event data with finitely many competing risks, the proportional hazards model has been a popular tool for relating the cause-specific outcomes to covariates [Prentice et al. Biometrics 34 (1978) 541--554]. This article studies an extension of this approach to allow a continuum of competing risks, in which the cause of failure is replaced by
Sun, Yanqing +2 more
openaire +5 more sources
Comparison of methods for estimating the attributable risk in the context of survival analysis
BMC Medical Research Methodology, 2017 Background The attributable risk (AR) measures the proportion of disease cases that can be attributed to an exposure in the population. Several definitions and estimation methods have been proposed for survival data.
Malamine Gassama +3 more
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A joint frailty model to estimate the recurrence process and the disease-specific mortality process without needing the cause of death. [PDF]
, 2014 In chronic diseases, such as cancer, recurrent events (such as relapses) are commonly observed; these could be interrupted by death. With such data, a joint analysis of recurrence and mortality processes is usually conducted with a frailty parameter ...
Roch Giorgi +8 more
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The importance of censoring in competing risks analysis of the subdistribution hazard
BMC Medical Research Methodology, 2017 Background The analysis of time-to-event data can be complicated by competing risks, which are events that alter the probability of, or completely preclude the occurrence of an event of interest.
Mark W. Donoghoe, Val Gebski
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Regression Models for Lifetime Data: An Overview
Stats, 2022 Two methods dominate the regression analysis of time-to-event data: the accelerated failure time model and the proportional hazards model. Broadly speaking, these predominate in reliability modelling and biomedical applications, respectively.
Chrys Caroni
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