Results 21 to 30 of about 1,821,882 (341)
Methods for non-proportional hazards in clinical trials: A systematic review [PDF]
Statistical Methods in Medical Research, 2023 For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable.
M. Bardo +15 more
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Generalized Proportional Reversed Hazards Model [PDF]
The Egyptian Statistical Journal, 2004 Inthis paper, we propose to generalized proportional reversed hazards model by Tsx*,t=[Ts,(X, t)]a, where Tsx*,tis baseline distribution func- tion and a is a positive real number.
M. Kayid, I. Elbatal
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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
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Statistics in Medicine, 2021 The Cox proportional hazards model is used extensively in clinical and epidemiological research. A key assumption of this model is that of proportional hazards.
P. Austin, Jiming Fang, Douglas S. Lee
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Survival analysis—part 2: Cox proportional hazards model
Indian Journal of Thoracic and Cardiovascular Surgery, 2021 Learning objectives: 1. To understand the log-rank test and limitations of the log-rank test in comparing survival between groups. 2. To understand the fundamental concepts of the proportional hazards assumption. 3.
S. Deo, V. Deo, V. Sundaram
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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
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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
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On Cox proportional hazards model performance under different sampling schemes.
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
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Interval-Censored Regression with Non-Proportional Hazards with Applications
Stats, 2023 Proportional hazards models and, in some situations, accelerated failure time models, are not suitable for analyzing data when the failure ratio between two individuals is not constant. We present a Weibull accelerated failure time model with covariables
Fábio Prataviera +4 more
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Pain and mortality among older adults in Korea [PDF]
Epidemiology and Health, 2021 OBJECTIVES With the increasing elderly population with chronic disease, understanding pain and designing appropriate policy interventions to it have become crucial.
Chiil Song, Wankyo Chung
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