Gene selection in Cox regression model based on  a new adaptive penalized method

The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases. Gene selection has been proved to be an effective way to improve the result of many methods. The Cox proportional hazards regression model is the most popular model in regression analysis for censored survival data. In this paper, an adaptive penalized Cox proportional hazards regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the Cox proportional hazards regression model with the weighted least absolute shrinkage and selection operator (LASSO) method. Experimental results show that the proposed method significantly outperforms two competitor methods in terms of the area under the curve and the number of the selected genes.

Download Full-text

Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis

BMC Medical Research Methodology ◽

10.1186/s12874-020-00945-9 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Colleen M. Sitlani ◽

Thomas Lumley ◽

Barbara McKnight ◽

Kenneth M. Rice ◽

Nels C. Olson ◽

...

Keyword(s):

Cohort Study ◽

Regression Model ◽

Proportional Hazards ◽

Cox Regression ◽

Cox Proportional Hazards ◽

Robust Methods ◽

Time To Event ◽

Sampling Weights ◽

Cox Proportional Hazards Regression ◽

Proportional Hazards Regression

Abstract Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers.

Download Full-text

Testing for Covariate Effect in the Cox Proportional Hazards Regression Model

Communication in Statistics- Theory and Methods ◽

10.1080/03610920802536958 ◽

2009 ◽

Vol 38 (14) ◽

pp. 2333-2347 ◽

Cited By ~ 6

Author(s):

Karthik Devarajan ◽

Nader Ebrahimi

Keyword(s):

Regression Model ◽

Proportional Hazards ◽

Cox Proportional Hazards ◽

Covariate Effect ◽

Cox Proportional Hazards Regression ◽

Proportional Hazards Regression

Download Full-text

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

FUDMA Journal of Sciences ◽

10.33003/fjs-2020-0404-496 ◽

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.

Download Full-text

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

Cells ◽

10.3390/cells8050428 ◽

2019 ◽

Vol 8 (5) ◽

pp. 428 ◽

Cited By ~ 3

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.

Download Full-text