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Weighted Cox Regression Using the R Package coxphw
Journal of Statistical Software, 2018 Cox's regression model for the analysis of survival data relies on the proportional hazards assumption. However, this assumption is often violated in practice and as a consequence the average relative risk may be under- or overestimated.
Daniela Dunkler +3 more
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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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Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression
BMC Bioinformatics, 2021 Background Important objectives in cancer research are the prediction of a patient’s risk based on molecular measurements such as gene expression data and the identification of new prognostic biomarkers (e.g. genes).
Katrin Madjar +3 more
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Censoring Balancing Functions for Undetected Probably Significant Effects in Cox Regression
International Journal of Mathematics and Mathematical Sciences, 2023 Weighted Cox regression models were proposed as an alternative to the standard Cox proportional hazards models where consistent estimators can be obtained with more relative strength compared to unweighted cases. We proposed censoring balancing functions
Ildephonse Nizeyimana +3 more
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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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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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Cox regression model under dependent truncation [PDF]
Biometrics, 2021 AbstractTruncation is a statistical phenomenon that occurs in many time‐to‐event studies. For example, autopsy‐confirmed studies of neurodegenerative diseases are subject to an inherent left and right truncation, also known as double truncation. When the goal is to study the effect of risk factors on survival, the standard Cox regression model cannot ...
Lior Rennert, Sharon X. Xie
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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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