scholarly journals A semi-parametric generalization of the Cox proportional hazards regression model: Inference and applications

2011 ◽  
Vol 55 (1) ◽  
pp. 667-676 ◽  
Author(s):  
Karthik Devarajan ◽  
Nader Ebrahimi
Keyword(s):  
Regression Model ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Model Inference ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression

MODELLING THE DETERMINANTS OF UNDER-FIVE CHILD MORTALITY RATES USING COX PROPORTIONAL HAZARDS REGRESSION MODEL

2021 ◽  
Vol 4 (4) ◽  
pp. 401-408
Author(s):  
M. C. Musa ◽  
O. E. Asiribo ◽  
H. G. Dikko ◽  
M. Usman ◽  
S. S. Sani
Keyword(s):  
Regression Model ◽  
Child Mortality ◽  
Proportional Hazards ◽  
Mortality Rates ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Predictor Variables ◽  
Under Five ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression

An under-five childhood mortality rates in Nigeria is still high, despite efforts of government at all levels to combat the menace. This study examined some factors that significantly affect under-five child mortality. A sample of mothers with children under the age of five from Nigeria Demographic and Health Survey data (NDHS, 2013 & 2018) was used to assess the effect of some selected predictor variables (or covariates) on childhood survival. Cox proportional hazards model is essentially a regression model popularly used for investigating the association between the survival time and one or more predictor variables. The results from final fitted Cox proportional hazards regression model that the covariates, contraceptive used by the mother, state of residence, birth weight of child and type of toilet facility used by the h-ousehold were found to be significantly associated with under-five survival in the North Central Region of Nigeria. All the calculations are performed using the R software for statistical analysis.

scholarly journals Prognostic Significance of Serum Free Amino Acids in Head and Neck Cancers

Cells ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 428 ◽  
Author(s):  
Vit Vsiansky ◽  
Marketa Svobodova ◽  
Jaromir Gumulec ◽  
Natalia Cernei ◽  
Dagmar Sterbova ◽  
...  
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Regression Model ◽  
Head And Neck ◽  
Cell Lines ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Transformed Cells ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression

Despite distinctive advances in the field of head and neck squamous cell cancer (HNSCC) biomarker discovery, the spectrum of clinically useful prognostic serum biomarkers is limited. As metabolic activities in highly proliferative transformed cells are fundamentally different from those in non-transformed cells, specific shifts in concentration of different metabolites may serve as diagnostic or prognostic markers. Blood amino acids have been identified as promising biomarkers in different cancers before, but little is known about this field in HNSCC. Blood amino acid profiles of 140 HNSCC patients were examined using high-performance liquid chromatography. Cox proportional hazards regression model was used to assess the prognostic value of amino acid concentrations in serum. Colony forming assay was used to identify the effect of amino acids that were significant in Cox proportional hazards regression models on colony forming ability of FaDu and Detroit 562 cell lines. In the multivariable Cox regression model for overall survival (OS), palliative treatment was associated with an unfavourable prognosis while high serum levels of methionine have had a positive prognostic impact. In the relapse-free survival (RFS) multivariable model, methionine was similarly identified as a positive prognostic factor, along with tumor localization in the oropharynx. Oral cavity localization and primary radio(chemo)therapy treatment strategy have been linked to poorer RFS. 1mM serine was shown to support the forming of colonies in both tested HNSCC cell lines. Effect of methionine was exactly the opposite.

Transient effects in the cox proportional hazards regression model

1995 ◽  
Vol 14 (14) ◽  
pp. 1553-1565 ◽  
Author(s):  
Elizabeth A. Mauger ◽  
Robert A. Wolfe ◽  
Friedrich K. Port
Keyword(s):  
Regression Model ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Transient Effects ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression

scholarly journals An Assessment of the Cox Proportional Hazards Regression Model for Epidemiologic Studies

Risk Analysis ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 777-794 ◽  
Author(s):  
Suresh H. Moolgavkar ◽  
Ellen T. Chang ◽  
Heather N. Watson ◽  
Edmund C. Lau
Keyword(s):  
Regression Model ◽  
Proportional Hazards ◽  
Epidemiologic Studies ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression

scholarly journals Salivary Metabolomics for Prognosis of Oral Squamous Cell Carcinoma

2022 ◽  
Vol 11 ◽  
Author(s):  
Shigeo Ishikawa ◽  
Masahiro Sugimoto ◽  
Tsuneo Konta ◽  
Kenichiro Kitabatake ◽  
Shohei Ueda ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Prognostic Factors ◽  
Prognostic Factor ◽  
Oral Squamous Cell Carcinoma ◽  
Regression Model ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Validation Group ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression

This study aimed to identify salivary metabolomic biomarkers for predicting the prognosis of oral squamous cell carcinoma (OSCC) based on comprehensive metabolomic analyses. Quantified metabolomics data of unstimulated saliva samples collected from patients with OSCC (n = 72) were randomly divided into the training (n = 35) and validation groups (n = 37). The training data were used to develop a Cox proportional hazards regression model for identifying significant metabolites as prognostic factors for overall survival (OS) and disease-free survival. Moreover, the validation group was used to develop another Cox proportional hazards regression model using the previously identified metabolites. There were no significant between-group differences in the participants’ characteristics, including age, sex, and the median follow-up periods (55 months [range: 3–100] vs. 43 months [range: 0–97]). The concentrations of 5-hydroxylysine (p = 0.009) and 3-methylhistidine (p = 0.012) were identified as significant prognostic factors for OS in the training group. Among them, the concentration of 3-methylhistidine was a significant prognostic factor for OS in the validation group (p = 0.048). Our findings revealed that salivary 3-methylhistidine is a prognostic factor for OS in patients with OSCC.

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