Results 61 to 70 of about 1,569,089 (229)
How Cox models react to a study-specific confounder in a patient-level pooled dataset: Random-effects better cope with an imbalanced covariate across trials unless baseline hazards differ [PDF]
arXiv, 2018 Combining patient-level data from clinical trials can connect rare phenomena with clinical endpoints, but statistical techniques applied to a single trial may become problematical when trials are pooled. Estimating the hazard of a binary variable unevenly distributed across trials showcases a common pooled database issue.
arxiv
Analyzing Non-proportional Hazards: Use of the MRH Package [PDF]
arXiv, 2016 In this manuscript we demonstrate the analysis of right-censored survival outcomes using the MRH package in R. The MRH package implements the multi-resolution hazard (MRH) model, which is a Polya-tree based, Bayesian semi-parametric method for flexible estimation of the hazard rate and covariate effects. The package allows for covariates to be included
arxiv
Informatics in Medicine Unlocked, 2023 Background: Diabetic retinopathy (DR) is one of the most common complications in type 2 diabetes (T2D) with an estimated prevalence of 22%. Predictive modelling has largely been dependent on Cox proportional hazards (CPH) with assumptions of linearity ...
Panu Looareesuwan +10 more
doaj
A transformer model for cause-specific hazard prediction
BMC BioinformaticsBackgroud Modelling discrete-time cause-specific hazards in the presence of competing events and non-proportional hazards is a challenging task in many domains.
Matthieu Oliver +4 more
doaj +1 more source
BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models
Journal of Statistical Software, 2020 We introduce a new R package, BeSS, for solving the best subset selection problem in linear, logistic and Cox's proportional hazard (CoxPH) models. It utilizes a highly efficient active set algorithm based on primal and dual variables, and supports ...
Canhong Wen +3 more
doaj +1 more source
PLoS ONEBackgroundIn recent years, remarkable progress has been made in the use of machine learning, especially in analyzing prognosis survival data. Traditional prediction models cannot identify interrelationships between factors, and the predictive accuracy is
Huan Zhang, Shan Zhao, Pengzhong Lv
doaj +1 more source
spBayesSurv: Fitting Bayesian Spatial Survival Models Using R
Journal of Statistical Software, 2020 Spatial survival analysis has received a great deal of attention over the last 20 years due to the important role that geographical information can play in predicting survival.
Haiming Zhou +2 more
doaj +1 more source
Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data [PDF]
arXiv, 2020 The classical approach to analyze time-to-event data, e.g. in clinical trials, is to fit Kaplan-Meier curves yielding the treatment effect as the hazard ratio between treatment groups. Afterwards commonly a log-rank test is performed in order to investigate whether there is a difference in survival, or, depending on additional covariates, a Cox ...
arxiv
Journal of Obesity & Metabolic SyndromeBackground : This study investigates the relationship between changes in physical activity levels and risk of metabolic syndrome. Methods : This study examined 1,686 adults aged 40 to 69 years from a community-based cohort study with complete 1st to 4th ...
Doo Yong Park +4 more
doaj +1 more source
Divergence-based robust inference under proportional hazards model for one-shot device life-test [PDF]
arXiv, 2020 In this paper, we develop robust estimators and tests for one-shot device testing under proportional hazards assumption based on divergence measures. Through a detailed Monte Carlo simulation study and a numerical example, the developed inferential procedures are shown to be more robust than the classical procedures, based on maximum likelihood ...
arxiv