Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths [PDF]
BMC Medical Informatics and Decision Making, 2022 Background Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about ...
Casper Wilstrup, Chris Cave
doaj +3 more sources
A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model [PDF]
Scientific Reports, 2022 There have been few investigations of cancer prognosis models based on Bayesian hierarchical models. In this study, we used a novel Bayesian method to screen mRNAs and estimate the effects of mRNAs on the prognosis of patients with lung adenocarcinoma ...
Na Sun +5 more
doaj +3 more sources
The Impact of Violation of the Proportional Hazards Assumption on the Calibration of the Cox Proportional Hazards Model [PDF]
Epidemiology, Biostatistics and Public HealthINTRODUCTION The Cox proportional hazards regression model is frequently used to develop clinical prediction models for time-to-event outcomes, allowing clinicians to estimate an individual’s risk of experiencing the outcome within specified time ...
Peter Austin, Daniele Giardiello
doaj +4 more sources
Moving beyond the Cox proportional hazards model in survival data analysis: a cervical cancer study [PDF]
BMJ Open, 2020 Objectives This study explored the prognostic factors and developed a prediction model for Chinese-American (CA) cervical cancer (CC) patients. We compared two alternative models (the restricted mean survival time (RMST) model and the proportional ...
Lixian Li +3 more
doaj +3 more sources
On Cox proportional hazards model performance under different sampling schemes. [PDF]
PLoS ONE, 2023 Cox's proportional hazards model (PH) is an acceptable model for survival data analysis. This work investigates PH models' performance under different efficient sampling schemes for analyzing time to event data (survival data). We will compare a modified
Hani Samawi, Lili Yu, JingJing Yin
doaj +3 more sources
Performance of goodness-of-fit tests for the Cox proportional hazards model with time-varying covariates. [PDF]
Lifetime Data Anal, 2014 Grant S, Chen YQ, May S.
europepmc +4 more sources
VERTICOX: Vertically Distributed Cox Proportional Hazards Model Using the Alternating Direction Method of Multipliers. [PDF]
IEEE Trans Knowl Data Eng, 2022 The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same ...
Dai W +5 more
europepmc +2 more sources
Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model [PDF]
Mathematics, 2021 Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others.
A. Ewees +8 more
semanticscholar +2 more sources
Quasi-linear Cox proportional hazards model with cross- L1 penalty [PDF]
BMC Medical Research Methodology, 2020 Background To accurately predict the response to treatment, we need a stable and effective risk score that can be calculated from patient characteristics.
Katsuhiro Omae, Shinto Eguchi
doaj +2 more sources
Adjusting for bias introduced by instrumental variable estimation in the Cox Proportional Hazards Model [PDF]
Biostatistics, 2017 Instrumental variable (IV) methods are widely used for estimating average treatment effects in the presence of unmeasured confounders. However, the capability of existing IV procedures, and most notably the two-stage residual inclusion (2SRI) procedure ...
Goodney, Philip P. +4 more
core +2 more sources