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Particle Swarm Optimization for Penalize cox models in long-term prediction of breast cancer data
Iraqi Journal for Computer Science and Mathematics, 2023 The particle swarm optimization algorithm (PSO) was applied to penalize the Cox model for predicting long-term outcomes of breast cancer patients.
Ehab Abbas, Basad Al-Sarray
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Testing and interpreting assumptions of COX regression analysis
Cancer Research, Statistics, and Treatment, 2019 The COX regression analysis is like any statistical test that is based on multiple assumptions. This is a guide for how to test the assumptions and how to interpret the results.
Sampada Dessai, Vijay Patil
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FAKTOR-FAKTOR YANG MEMPENGARUHI DAYA TAHAN MAHASISWA TEKNIK SIPIL UIB DALAM MEMPERTAHANKAN STUDINYA
E-Jurnal Matematika, 2021 Linear regression model cannot be used to analyze the relationship between survival time and independent variables, it is because the linear regression model is not able to handle censored data.
MAHFUZ HUDORI
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A Federated Cox Model with Non-proportional Hazards
, 2022 Recent research has shown the potential for neural networks to improve upon classical survival models such as the Cox model, which is widely used in clinical practice. Neural networks, however, typically rely on data that are centrally available, whereas healthcare data are frequently held in secure silos.
D. Kai Zhang +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 ...
Fahrmeir, Ludwig, Kneib, Thomas
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An exact corrected log-likelihood function for Cox's proportional hazards model under measurement error and some extensions [PDF]
, 2002 This paper studies Cox`s proportional hazards model under covariate measurement error. Nakamura`s (1990) methodology of corrected log-likelihood will be applied to the so called Breslow likelihood, which is, in the absence of measurement error ...
Augustin, Thomas
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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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Statistical models for the analysis of skewed healthcare cost data: A simulation study [PDF]
, 2015 Skewed data is the main issue in statistical models in healthcare costs. Data transformation is a conventional method to decrease skewness, but there are some disadvantages.
Angali, Kambiz Ahmadi +2 more
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Journal of Biostatistics and Epidemiology, 2020 Background: The Cox proportional hazard model has gained ground in Biostatistics and other related fields. It has been extended to capture different scenarios, part of which are violation of the proportionality of the hazards, presence of time dependent ...
Bayowa Teniola Babalola +1 more
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Health Science Reports, 2023 Background and Aims Cervical cancer is the fourth most common cause of cancer‐related death in the world. The objective of this study was to determine factors that affect the longitudinal change of tumor size and the time to death of outpat Methods A ...
Aragaw E. Aguade +2 more
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