scholarly journals Lagged Duration Dependence in Mixed Proportional Hazard Models

2011 ◽  
Author(s):  
Matteo Picchio
Keyword(s):  
Hazard Models ◽  
Duration Dependence ◽  
Proportional Hazard ◽  
Proportional Hazard Models ◽  
Mixed Proportional Hazard Models

SEMI-NONPARAMETRIC INTERVAL-CENSORED MIXED PROPORTIONAL HAZARD MODELS: IDENTIFICATION AND CONSISTENCY RESULTS

2008 ◽  
Vol 24 (3) ◽  
pp. 749-794 ◽  
Author(s):  
Herman J. Bierens
Keyword(s):  
Legendre Polynomials ◽  
Unobserved Heterogeneity ◽  
Unit Interval ◽  
Hazard Models ◽  
Nonparametric Identification ◽  
Proportional Hazard ◽  
Proportional Hazard Models ◽  
Consistency Results ◽  
Interval Censored ◽  
Mixed Proportional Hazard Models

In this paper I propose estimating distributions on the unit interval semi-nonparametrically using orthonormal Legendre polynomials. This approach will be applied to the interval-censored mixed proportional hazard (ICMPH) model, where the distribution of the unobserved heterogeneity is modeled semi-nonparametrically. Various conditions for the nonparametric identification of the ICMPH model are derived. I will prove general consistency results for M-estimators of (partly) non-euclidean parameters under weak and easy-to-verify conditions and specialize these results to sieve estimators. Special attention is paid to the case where the support of the covariates is finite.

scholarly journals LAMB1 Is Related to the T Stage and Indicates Poor Prognosis in Gastric Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382110049
Author(s):  
Tao Ran ◽  
ZhiJi Chen ◽  
LiWen Zhao ◽  
Wei Ran ◽  
JinYu Fan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Poor Prognosis ◽  
Hazard Models ◽  
Proportional Hazard ◽  
Kegg Analysis ◽  
Proportional Hazard Models ◽  
Cox Proportional Hazard ◽  
Kaplan Meier ◽  
Cox Proportional Hazard Models ◽  
Gastric Cancer Patients

Background and Objective: Gastric cancer (GC) is a common tumor malignancy with high incidence and poor prognosis. Laminin is an indispensable component of basement membrane and extracellular matrix, which is responsible for bridging the internal and external environment of cells and transmitting signals. This study mainly explored the association of the LAMB1 expression with clinicopathological characteristics and prognosis in gastric cancer. Methods: The expression data and clinical information of gastric cancer patients were downloaded from The Cancer Genome Atlas (TCGA) and Asian Cancer Research Group (ACRG). And we analyzed the relationship between LAMB1 expression and clinical characteristics through R. CIBERSORTx was used to calculate the absolute score of immune cells in gastric tumor tissues. Then COX proportional hazard models and Kaplan-Meier curves were performed to evaluate the role of LAMB1 and its influence on prognosis in gastric cancer patients. Finally, GO and KEGG analysis were applied for LAMB1-related genes in gastric cancer, and PPI network was constructed in Cytoscape software. Results: In the TCGA cohort, patients with gastric cancer frequently generated LAMB1 gene copy number variation, but had little effect on mRNA expression. Both in the TCGA and ACRG cohorts, the mRNA expression of LAMB1 in gastric cancer tissues was higher than it in normal tissues. All patients were divided into high expression group and low expression group according to the median expression level of LAMB1. The elevated expression group obviously had more advanced cases and higher infiltration levels of M2 macrophages. COX proportional hazard models and Kaplan-Meier curves revealed that patients with enhanced expression of LAMB1 have a worse prognosis. GO/KEGG analysis showed that LAMB1-related genes were enriched in PI3K-Akt signaling pathway, focal adhesion, ECM-receptor interaction, etc. Conclusions: The high expression of LAMB1 in gastric cancer is related to the poor prognosis of patients, and it may be related to microenvironmental changes in tumors.

scholarly journals Low rather than high mean corpuscular volume is associated with mortality in Japanese patients under hemodialysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hirokazu Honda ◽  
Miho Kimachi ◽  
Noriaki Kurita ◽  
Nobuhiko Joki ◽  
Masaomi Nangaku
Keyword(s):  
Mean Corpuscular Volume ◽  
Japanese Patients ◽  
Cubic Spline ◽  
Hazard Models ◽  
Proportional Hazard ◽  
Corpuscular Volume ◽  
Proportional Hazard Models ◽  
Cox Proportional Hazard ◽  
All Cause Mortality ◽  
Cox Proportional Hazard Models

Abstract Recent studies have reported that high mean corpuscular volume (MCV) might be associated with mortality in patients with advanced chronic kidney disease (CKD). However, the question of whether a high MCV confers a risk for mortality in Japanese patients remains unclear. We conducted a longitudinal analysis of a cohort of 8571 patients using data derived from the Japan Dialysis Outcomes and Practice Patterns Study (J-DOPPS) phases 1 to 5. Associations of all-cause mortality, vascular events, and hospitalization due to infection with baseline MCV were examined via Cox proportional hazard models. Non-linear relationships between MCV and these outcomes were examined using restricted cubic spline analyses. Associations between time-varying MCV and these outcomes were also examined as sensitivity analyses. Cox proportional hazard models showed a significant association of low MCV (< 90 fL), but not for high MCV (102 < fL), with a higher incidence of all-cause mortality and hospitalization due to infection compared with 94 ≤ MCV < 98 fL (reference). Cubic spline analysis indicated a graphically U-shaped association between baseline MCV and all-cause mortality (p for non-linearity p < 0.001). In conclusion, a low rather than high MCV might be associated with increased risk for all-cause mortality and hospitalization due to infection among Japanese patients on hemodialysis.

A health condition model for wind turbine monitoring through neural networks and proportional hazard models

2017 ◽  
Vol 231 (5) ◽  
pp. 481-494 ◽  
Author(s):  
Peyman Mazidi ◽  
Mian Du ◽  
Lina Bertling Tjernberg ◽  
Miguel A Sanz Bobi
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Real Time ◽  
Condition Monitoring ◽  
Health Condition ◽  
Hazard Models ◽  
Proportional Hazard ◽  
Neural Network Models ◽  
Proportional Hazard Models ◽  
Condition Model

In this article, a parametric model for health condition monitoring of wind turbines is developed. The study is based on the assumption that a wind turbine’s health condition can be modeled through three features: rotor speed, gearbox temperature and generator winding temperature. At first, three neural network models are created to simulate normal behavior of each feature. Deviation signals are then defined and calculated as accumulated time-series of differences between neural network predictions and actual measurements. These cumulative signals carry health condition–related information. Next, through nonlinear regression technique, the signals are used to produce individual models for considered features, which mathematically have the form of proportional hazard models. Finally, they are combined to construct an overall parametric health condition model which partially represents health condition of the wind turbine. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The health condition monitoring of wind turbine model has capability of evaluating real-time and overall health condition of a wind turbine which can also be used with regard to maintenance in electricity generation in electric power systems. The model also has flexibility to overcome current challenges such as scalability and adaptability. The model is verified in illustrating changes in real-time and overall health condition with respect to considered anomalies by testing through actual and artificial data.

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