Gene selection for survival data under dependent censoring: A copula-based approach

Dependent censoring arises in biomedical studies when the survival outcome of interest is censored by competing risks. In survival data with microarray gene expressions, gene selection based on the univariate Cox regression analyses has been used extensively in medical research, which however, is only valid under the independent censoring assumption. In this paper, we first consider a copula-based framework to investigate the bias caused by dependent censoring on gene selection. Then, we utilize the copula-based dependence model to develop an alternative gene selection procedure. Simulations show that the proposed procedure adjusts for the effect of dependent censoring and thus outperforms the existing method when dependent censoring is indeed present. The non-small-cell lung cancer data are analyzed to demonstrate the usefulness of our proposal. We implemented the proposed method in an R “compound.Cox” package.

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Gene Selection for Microarray Cancer Data Classification by a Novel Rule-Based Algorithm

Information ◽

10.3390/info9010006 ◽

2018 ◽

Vol 9 (1) ◽

pp. 6 ◽

Cited By ~ 8

Author(s):

Adrian Pino Angulo

Keyword(s):

Gene Selection ◽

Data Classification ◽

Rule Based ◽

Cancer Data ◽

Selection For

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Gastric cancer in East Azerbaijan, Iran: Five-year survival analysis of population-based cancer registry results

Biomedical Research and Therapy ◽

10.15419/bmrat.v7i11.648 ◽

2020 ◽

Vol 7 (11) ◽

pp. 4114-4121

Author(s):

Pooneh Jabbaripour ◽

Mohammad Hossein Somi ◽

Hossein Mashhadi Abdolahi ◽

Roya Dolatkhah

Keyword(s):

Gastric Cancer ◽

Survival Analysis ◽

Cancer Registry ◽

Survival Data ◽

Cox Regression ◽

Survival Function ◽

Population Based ◽

Rank Test ◽

Cancer Data ◽

East Azerbaijan

Introduction: Gastric cancer is the most common cancer with significant increasing trends during the last decade in Iran. The aim of this study was to evaluate the epidemiologic profile of gastric cancer along with gastric cancer-specific survival analysis. Methods: This was an analytical cross-sectional study in which all gastric cancer data were analyzed using the database of the East Azerbaijan Population-Based Cancer Registry (EA-PBCR). The incidents of definitive gastric cancer diagnosis were between the period of March 20th, 2015 to March 19th, 2017 ( = 3 Iranian solar years). The survival analysis was performed using the Kaplan-Meier method and life tables for 1- to 5-year survival data. The Log-rank test and Cox regression were computed to test the equality of survival function and mortality hazard. Results: Overall, 2,631 newly diagnosed gastric cancer cases were registered for 3 years. Gastric cancer was 2.35 times more common in men than women. The most common age group was the 7th decade- with 531 (31.2%) gastric cancer cases. Most of the gastric cancer cases were non-cardia (n = 2,244, 85.29%) cancer, and the proportion of non-cardia to cardia gastric cancer was 5.8:1. Overall survival was 60.1%, and 1- to 5-year survival proportions were 91.61%, 64.21%, 58.53%, 30.14% and 24.77%, respectively. Cardia cancers had a worse survival rate than non-cardia cancers, and the hazard of mortality was 1.33 times higher in cardia than non-cardia cancers (hazard ratio or HR = 1.33; 95% CI: 1.05 - 1.68; P = 0.017). Conclusion: Non-cardia gastric cancer is still the most dominant subsite in East Azerbaijan, Iran. There was a higher 1- to 5- year survival proportion in East Azerbaijan, with lower overall mortality rates, compared to other regions of Iran.

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Gene selection of non-small cell lung cancer data for adjuvant chemotherapy decision using cell separation algorithm

Applied Intelligence ◽

10.1007/s10489-020-01740-1 ◽

2020 ◽

Vol 50 (11) ◽

pp. 3822-3836

Author(s):

Najmeh Sadat Jaddi ◽

Mohammad Saniee Abadeh

Keyword(s):

Lung Cancer ◽

Adjuvant Chemotherapy ◽

Small Cell Lung Cancer ◽

Cell Separation ◽

Gene Selection ◽

Small Cell ◽

Small Cell Lung ◽

Separation Algorithm ◽

Cancer Data ◽

Selection Of

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

International Journal of Advanced Statistics and Probability ◽

10.14419/ijasp.v8i1.30566 ◽

2020 ◽

Vol 8 (1) ◽

pp. 16

Author(s):

Oday Isam Alskal ◽

Zakariya Yahya Algamal

Keyword(s):

Regression Model ◽

Survival Data ◽

Proportional Hazards ◽

Gene Selection ◽

Cox Regression ◽

Area Under The Curve ◽

Cox Proportional Hazards ◽

Cox Proportional Hazards Regression ◽

Proportional Hazards Regression ◽

Selection Operator

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.

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Model evaluation and variable selection for interval-censored data

10.32469/10355/46846 ◽

2015 ◽

Author(s):

◽

Tyler Cook

Keyword(s):

Survival Analysis ◽

Variable Selection ◽

Survival Time ◽

Censored Data ◽

Survival Data ◽

Failure Time ◽

Dependent Censoring ◽

Interval Censored Data ◽

Interval Censored ◽

Selection For

Survival analysis is a popular area of statistics dealing with time-to-event data. A special characteristic of survival data is the presence of censoring. Censoring occurs when the survival time is only partially known. In medical studies, censoring can be caused by patients dropping out of the study before their disease event occurs. This dissertation focuses on the analysis of interval-censored data, where the failure time is only known to belong to some interval of observation times. One problem researchers face when analyzing survival data is how to handle the censoring distribution. This is an important consideration because sometimes a patient's survival time is related to the time they drop out of the study. It is often assumed that these two times are unrelated, so special methods need to be developed when they are dependent. Part of this dissertation investigates the effectiveness of methods developed for interval-censored data with dependent censoring when the censoring is actually independent. The results of these simulation studies can provide guidelines for deciding between models when facing a practical problem where one is unsure about the dependence of the censoring distribution. Another important problem seen in survival analysis is variable selection. For example, doctors might want to identify a set of diagnostic tests or measurements that can predict patient survival. We propose an imputation approach for variable selection of interval-censored data that utilizes penalized likelihood procedures. This work is significant because researchers currently do not have many tools to select important variables related to the survival time for interval-censored data.

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Bayesian variable selection for the Cox regression model with spatially varying coefficients with applications to Louisiana respiratory cancer data

Biometrical Journal ◽

10.1002/bimj.202000047 ◽

2021 ◽

Author(s):

Jinjian Mu ◽

Qingyang Liu ◽

Lynn Kuo ◽

Guanyu Hu

Keyword(s):

Variable Selection ◽

Regression Model ◽

Cox Regression ◽

Bayesian Variable Selection ◽

Cox Regression Model ◽

Cancer Data ◽

Varying Coefficients ◽

Respiratory Cancer ◽

Selection For ◽

Spatially Varying

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Identifying Measurement Invariant Item Sets in Cross-Cultural Settings Using an Automated Item Selection Procedure

Methodology ◽

10.1027/1614-2241/a000155 ◽

2018 ◽

Vol 14 (4) ◽

pp. 177-188 ◽

Cited By ~ 2

Author(s):

Martin Schultze ◽

Michael Eid

Keyword(s):

Measurement Invariance ◽

Optimal Solution ◽

Selection Procedure ◽

Cross Cultural ◽

Item Selection ◽

Ant System ◽

Item Quality ◽

Ant System Algorithm ◽

Selection For ◽

Selection Of

Abstract. In the construction of scales intended for the use in cross-cultural studies, the selection of items needs to be guided not only by traditional criteria of item quality, but has to take information about the measurement invariance of the scale into account. We present an approach to automated item selection which depicts the process as a combinatorial optimization problem and aims at finding a scale which fulfils predefined target criteria – such as measurement invariance across cultures. The search for an optimal solution is performed using an adaptation of the [Formula: see text] Ant System algorithm. The approach is illustrated using an application to item selection for a personality scale assuming measurement invariance across multiple countries.

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