Results 31 to 40 of about 800,848 (376)
Bayesian semiparametric inference for multivariate doubly-interval-censored data [PDF]
, 2010 Based on a data set obtained in a dental longitudinal study, conducted in Flanders (Belgium), the joint time to caries distribution of permanent first molars was modeled as a function of covariates.
De Iorio, Maria +3 more
core +4 more sources
Comparison of radiomic feature aggregation methods for patients with multiple tumors
Scientific Reports, 2021 Radiomic feature analysis has been shown to be effective at analyzing diagnostic images to model cancer outcomes. It has not yet been established how to best combine radiomic features in cancer patients with multifocal tumors.
Enoch Chang +7 more
doaj +1 more source
Proportional hazards models with discrete frailty [PDF]
Lifetime Data Analysis, 2010 We extend proportional hazards frailty models for lifetime data to allow a negative binomial, Poisson, Geometric or other discrete distribution of the frailty variable. This might represent, for example, the unknown number of flaws in an item under test.
Caroni, Chrys +2 more
openaire +4 more sources
Trend-constrained corrected score for proportional hazards model with covariate measurement error
Contemporary Clinical Trials Communications, 2015 In many medical research studies, survival time is typically the primary outcome of interest. The Cox proportional hazards model is the most popular method to investigate the relationship between covariates and possibly right-censored survival time ...
Ming Zhu, Yijian Huang
doaj +1 more source
DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network [PDF]
BMC Medical Research Methodology, 2016 BackgroundMedical practitioners use survival models to explore and understand the relationships between patients’ covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear
Jared Katzman +5 more
semanticscholar +1 more source
Regression Models for Lifetime Data: An Overview
Stats, 2022 Two methods dominate the regression analysis of time-to-event data: the accelerated failure time model and the proportional hazards model. Broadly speaking, these predominate in reliability modelling and biomedical applications, respectively.
Chrys Caroni
doaj +1 more source
, 2018 The Cox model, which remains as the first choice in analyzing time-to-event data even for large datasets, relies on the proportional hazards assumption. When the data size exceeds the computer memory, the standard statistics for testing the proportional ...
Schifano, Elizabeth D. +3 more
core +1 more source
Explained randomness in proportional hazards models [PDF]
Statistics in Medicine, 2005 A coefficient of explained randomness, analogous to explained variation but for non-linear models, was presented by Kent. The construct hinges upon the notion of Kullback-Leibler information gain. Kent and O'Quigley developed these ideas, obtaining simple, multiple and partial coefficients for the situation of proportional hazards regression.
John, O'Quigley +2 more
openaire +2 more sources
Regularization for Cox's proportional hazards model with NP-dimensionality
, 2012 High throughput genetic sequencing arrays with thousands of measurements per sample and a great amount of related censored clinical data have increased demanding need for better measurement specific model selection.
Bradic, Jelena +2 more
core +1 more source
BMC Musculoskeletal Disorders, 2021 Background Survival analysis and effect of covariates on survival time is a central research interest. Cox proportional hazards regression remains as a gold standard in the survival analysis. The Cox model relies on the assumption of proportional hazards
I. Kuitunen +4 more
semanticscholar +1 more source