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Non-proportional hazards models in survival analysis
2000Cox’ proportional hazard model is usually the model of choice in survival analysis. It is shown that this model can be embedded in a GLMmodel by proper discretization of the time axis. That approach easily allows non-proportional hazard models, that are special cases of time-varying coefficients models.
Hans C. van Houwelingen +1 more
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A Non‐Proportional Hazards Model with Hazard Ratio Functions Free from Covariate Values
International Statistical Review, 2020SummaryA brief survey on methods to handle non‐proportional hazards in survival analysis is given with emphasis on short‐term and long‐term hazard ratio modelling. A drawback of the existing model of this nature is that except at time zero or infinity, the hazard ratio for a unit increase in the value of a covariate depends on the starting value.
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Estimation of Main Effect When Covariates Have Non-Proportional Hazards
Communications in Statistics - Simulation and Computation, 2014The Cox proportional hazards (PH) regression model has been widely used to analyze survival data in clinical trials and observational studies. In addition to estimating the main treatment or exposure group effect, it is common to adjust for additional covariates using the Cox model.
Erika Strandberg, Xinyi Lin, Ronghui Xu
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The Hazard of Non-proportional Hazards in Time to Event Analysis
European Journal of Vascular and Endovascular Surgery, 2021Lorenz Meuli, Christoph Kuemmerli
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On a non‐proportional hazards regression model for repeated medical random counts
Statistics in Medicine, 1997A wholly parametric non-proportional hazards survival model is introduced. The model retains Cox's constant of proportionality as the leading term in the relative risk but permits additional flexibility by modelling the relative risk as a function of time.
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Sample Size Determination Under Non-proportional Hazards
2019The proportional hazards assumption rarely holds in clinical trials of cancer immunotherapy. Specifically, delayed separation of the Kaplan-Meier survival curves and long-term survival have been observed. Routine practice in designing a randomized controlled two-arm clinical trial with a time-to-event endpoint assumes proportional hazards.
Miao Yang +2 more
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Canadian Journal of Statistics, 2016
AbstractPrediction of a cause‐specific cumulative incidence function (CIF) for data containing competing risks is of primary interest to clinicians when making treatment decisions for patients given their prognostic characteristics. The Fine–Gray regression model is widely used to incorporate multiple prognostic factors, yet it is not applicable when ...
Liu, Qing +3 more
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AbstractPrediction of a cause‐specific cumulative incidence function (CIF) for data containing competing risks is of primary interest to clinicians when making treatment decisions for patients given their prognostic characteristics. The Fine–Gray regression model is widely used to incorporate multiple prognostic factors, yet it is not applicable when ...
Liu, Qing +3 more
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Analysis of Multivariate Survival Times with Non-Proportional Hazards Models
1997In a clinical trial to evaluate treatments for a chronic disease, a commonly used regression method for analyzing multiple event times is based on a multivariate Cox model (Wei, Lin and Weissfeld, 1989). However, the Cox model may not fit the data well.
L. Chen, L. J. Wei
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A non-proportional hazards cure model with an application to gastric cancer data analysis
Statistical Methods in Medical ResearchIn many practical situations, some subjects may never experience the event of interest in their lifetime. These subjects are referred to as the cured or non-susceptible subjects. In the context of chronic disease treatment, this is referred to as a cure fraction.
N Balakrishnan +2 more
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