Results 31 to 40 of about 3,057,200 (307)
Journal of Multivariate Analysis, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shoubhik Mondal, Sundarraman Subramanian
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Cox Regression with Dependent Error in Covariates [PDF]
Biometrics, 2017 SummaryMany survival studies have error-contaminated covariates due to the lack of a gold standard of measurement. Furthermore, the error distribution can depend on the true covariates but the structure may be difficult to characterize; heteroscedasticity is a common manifestation.
Yijian Huang, Ching-Yun Wang
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陆军军医大学学报, 2023 Objective To build a stacked supervised autoencoder (SSAE) model based on transcriptomic data, so as to improve the prognostic prediction of lung adenocarcinoma (LUAD).
LI Pengpeng +3 more
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Molecular Insights and Prognosis Associated With RBM8A in Glioblastoma
Frontiers in Molecular Biosciences, 2022 Background: Glioblastoma (GBM) is the most invasive brain tumors, and it is associated with high rates of recurrence and mortality. The purpose of this study was to investigate the expression of RBM8A in GBM and the potential influence of its expression ...
Lei Wei +7 more
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Building Cox-Type Structured Hazard Regression Models with Time-Varying Effects [PDF]
, 2008 In recent years, flexible hazard regression models based on penalised splines have been developed that allow us to extend the classical Cox-model via the inclusion of time-varying and nonparametric effects.
Küchenhoff, Helmut +3 more
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Survival analysis and Cox regression
Allergologia et Immunopathologia, 2011 The data provided by clinical trials are often expressed in terms of survival. The analysis of survival comprises a series of statistical analytical techniques in which the measurements analysed represent the time elapsed between a given exposure and the outcome of a certain event.
N, Benítez-Parejo +2 more
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A mixed model approach for structured hazard regression [PDF]
, 2004 The classical Cox proportional hazards model is a benchmark approach to analyze continuous survival times in the presence of covariate information. In a number of applications, there is a need to relax one or more of its inherent assumptions, such as ...
Thomas Kneib +4 more
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Medicina, 2020 This study purposed to validate the accuracy of an artificial neural network (ANN) model for predicting the mortality after hip fracture surgery during the study period, and to compare performance indices between the ANN model and a Cox regression model.
Cheng-Yen Chen +4 more
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Cox's Periodic Regression Model
The Annals of Statistics, 1988 Cox's regression model has been successfully used for censored survival data. It can be adapted to model a counting process having a periodic underlying intensity. In survival analysis, the asymptotic properties, as studied by \textit{P. K. Andersen} and \textit{R. D. Gill} [ibid.
Pons, O., de Turckheim, E.
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Survival analysis: Part I — analysis of time-to-event [PDF]
Korean Journal of Anesthesiology, 2018 Length of time is a variable often encountered during data analysis. Survival analysis provides simple, intuitive results concerning time-to-event for events of interest, which are not confined to death.
Junyong In, Dong Kyu Lee
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