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Quality and Reliability Engineering International, 2020
The Bayesian network (BN) is an efficient tool for probabilistic modeling and causal inference, and it has gained considerable attentions in the field of reliability assessment.
Yanfeng Li +4 more
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The Bayesian network (BN) is an efficient tool for probabilistic modeling and causal inference, and it has gained considerable attentions in the field of reliability assessment.
Yanfeng Li +4 more
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Misspecified Proportional Hazard Models
Biometrika, 1986Let \((N_ i(t)\), \(t\geq 0\), \(i=1,...,n)\) be a counting process in which \(N_ i(t)\) records the number of failures in [0,t] for i-th item, and let \(Y_ i(t)\lambda_ 0(t) \exp (\beta Z_ i)\) \((i=1,...,n)\) be a random intensity process (for \(N_ i)\).
Struthers, C. A., Kalbfleisch, J. D.
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Tree-Structured Proportional Hazards Regression Modeling
Biometrics, 1994A method for fitting piecewise proportional hazards models to censored survival data is described. Stratification is performed recursively, using a combination of statistical tests and residual analysis. The bootstrap is employed to keep the probability of a Type I error (the error of discovering two or more strata when there is only one) of the method
Ahn, Hongshik, Loh, Wei-Yin
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Mediation analysis with causally ordered mediators using Cox proportional hazards model
Statistics in Medicine, 2018Causal mediation analysis aims to investigate the mechanism linking an exposure and an outcome. However, studies regarding mediation effects on survival outcomes are limited, particularly in multi‐mediator settings.
Shu-Hsien Cho, Yen-Tsung Huang
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2008
The estimation of duration models has been the subject of significant research in econometrics since the late 1970s. Cox (1972) proposed the use of proportional hazard models in biostatistics and they were soon adopted for use in economics. Since Lancaster (1979), it has been recognized among economists that it is important to account for unobserved ...
Jerry A. Hausman, Tiemen M. Woutersen
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The estimation of duration models has been the subject of significant research in econometrics since the late 1970s. Cox (1972) proposed the use of proportional hazard models in biostatistics and they were soon adopted for use in economics. Since Lancaster (1979), it has been recognized among economists that it is important to account for unobserved ...
Jerry A. Hausman, Tiemen M. Woutersen
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2021
We consider several models that describe survival in the presence of observable covariates, these covariates measuring subject heterogeneity. The most general situation can be described by a model with a parameter of high, possibly unbounded, dimension.
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We consider several models that describe survival in the presence of observable covariates, these covariates measuring subject heterogeneity. The most general situation can be described by a model with a parameter of high, possibly unbounded, dimension.
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Survival Analysis with Cox Proportional Hazards Model in Predicting Patient Outcomes
International Conference Electronic Systems, Signal Processing and Computing Technologies [ICESC-]Survival analysis is crucial for understanding the factors that influence patient outcomes across time. The objective is to predict the outcomes of patient survival under various circumstances using the Cox Proportional Hazards Model. The main objectives
Monikapreethi S K +5 more
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2011
This chapter discusses the most widely used regression models in competing risks. Following an introduction in Section 5.1, Section 5.2 discusses proportional cause-specific hazards models, and Section 5.3 discusses the proportional subdistribution hazards model. The cause-specific hazards are as defined in Chapter 3.
Jan Beyersmann +2 more
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This chapter discusses the most widely used regression models in competing risks. Following an introduction in Section 5.1, Section 5.2 discusses proportional cause-specific hazards models, and Section 5.3 discusses the proportional subdistribution hazards model. The cause-specific hazards are as defined in Chapter 3.
Jan Beyersmann +2 more
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Regression analysis of bivariate current status data under the proportional hazards model
, 2017This article discusses the regression analysis of bivariate current status or case I interval‐censored failure time data under the marginal proportional hazards model.
T. Hu, Qingning Zhou, Jianguo Sun
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