Results 301 to 310 of about 753,638 (377)
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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 \(N_ i)\).
C. A. Struthers, John D. Kalbfleisch
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A Proportional Hazards Model for the Subdistribution of a Competing Risk
, 1999With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption.
J. Fine, R. Gray
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The Identifiability of the Proportional Hazard Model
The Review of Economic Studies, 1984Summary: This paper presents new identifiability conditions for the Cox proportional hazard model [see \textit{D. R. Cox}, J. R. Stat. Soc., Ser. B 34, 187-220 (1972; Zbl 0243.62041)] for duration data when unobserved person specific variables are present. We compare our conditions with those presented by \textit{C. Elbers} and \textit{G. Ridder} [Rev.
Burton S. Singer, James J. Heckman
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The Robust Inference for the Cox Proportional Hazards Model
, 1989We derive the asymptotic distribution of the maximum partial likelihood estimator β for the vector of regression coefficients β under a possibly misspecified Cox proportional hazards model.
D. Lin, L. J. Wei
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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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Model misspecification in proportional hazards regression
Biometrika, 1995Summary: The proportional hazards model is frequently used to evaluate the effect of treatment on failure time events in randomised clinical trials. Concomitant variables are usually available and may be considered for use in the primary analyses under the assumption that incorporating them may reduce bias or improve efficiency.
Garnet L. Anderson, Thomas R. Fleming
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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 ...
Tiemen Woutersen, Jerry A. Hausman
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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 ...
Tiemen Woutersen, Jerry A. Hausman
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Masking Unmasked in the Proportional Hazards Model
Biometrics, 2000Summary.Influence measures based on the pairwise deletion approach and the differentiation approach are developed for unmasking observations masked by other observations in the proportional hazards model. These influential observations might have substantial impact on statistical inference and might provide important information for model adequacy. One
Wen Hsiang Wei, Michael R. Kosorok
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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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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.
openaire +2 more sources