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Cox regression survival analysis with compositional covariates: application to modelling mortality risk from 24-h physical activity patterns [PDF]
, 2020 Survival analysis is commonly conducted in medical and public health research to assess the association of an exposure or intervention with a hard end outcome such as mortality. The Cox (proportional hazards) regression model is probably the most popular
Chastin, S.F.M. +4 more
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Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 2020 Introduction The frequently used Cox regression applies two critical assumptions, which might not hold for all predictors. In this study, the results from a Cox regression model (CM) and a generalized Cox regression model (GCM) are compared. Methods Data
Jantje Goerdten +2 more
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Factors associated with methadone treatment duration: a Cox regression analysis. [PDF]
PLoS ONE, 2015 This study examined retention rates and associated predictors of methadone maintenance treatment (MMT) duration among 128 newly admitted patients in Taiwan. A semi-structured questionnaire was used to obtain demographic and drug use history.
Chao-Kuang Lin +4 more
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Weighted Cox regression for the prediction of heterogeneous patient subgroups
BMC Medical Informatics and Decision Making, 2021 Background An important task in clinical medicine is the construction of risk prediction models for specific subgroups of patients based on high-dimensional molecular measurements such as gene expression data.
Katrin Madjar, Jörg Rahnenführer
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Cox regression with linked data
Statistics in Medicine, 2023 Record linkage is increasingly used, especially in medical studies, to combine data from different databases that refer to the same entities. The linked data can bring analysts novel and valuable knowledge that is impossible to obtain from a single database.
Vo, Thanh Huan +6 more
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COX PROPORTIONAL HAZARD REGRESSION SURVIVAL ANALYSIS FOR TYPE 2 DIABETES MELITUS
Barekeng, 2022 One of the most widely used methods of survival analysis is Cox proportional hazard regression. It is a semiparametric regression used to investigate the effects of a number of variables on the dependent variable based on survival time.
Umi Mahmudah +4 more
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Beyond first-order asymptotics for Cox regression [PDF]
, 2015 To go beyond standard first-order asymptotics for Cox regression, we develop parametric bootstrap and second-order methods. In general, computation of $P$-values beyond first order requires more model specification than is required for the likelihood ...
Bellio, Ruggero, Pierce, Donald A.
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Stepwise cox regression analysis in SPSS
Cancer Research, Statistics, and Treatment, 2018 This article is a beginners' guide for performing Cox regression analysis in SPSS. The article provides practical steps toward performing Cox analysis and interpreting the output of SPSS for Cox regression analysis.
Sampada Dessai, Vijai Simha, Vijay Patil
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Implementation of complex interactions in a Cox regression framework [PDF]
, 2003 The standard Cox proportional hazards model has been extended by functionally describable interaction terms. The first of which are related to neural networks by adopting the idea of transforming sums of weighted covariables by means of a logistic ...
Müller, M., Ulm, Kurt
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Model Uncertainty Quantification in Cox Regression
Biometrics, 2023 Abstract We consider covariate selection and the ensuing model uncertainty aspects in the context of Cox regression. The perspective we take is probabilistic, and we handle it within a Bayesian framework. One of the critical elements in variable/model selection is choosing a suitable prior for model parameters.
Gonzalo García-Donato +2 more
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