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Journal of Biostatistics and Epidemiology، جلد ۱۱، شماره ۱، صفحات ۶۳-۸۱
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| عنوان انگلیسی |
Sparse Variable Selection in Competing Risks Additive Hazards Regression: An application for identifying biomarkers related to prognosis of Bladder Cancer |
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| چکیده انگلیسی مقاله |
Introduction: Variable selection has become an increasingly important topic in biomedical research, as evidenced by its modern applications in high-throughput genomic data analysis. Specifically, interest in analyzing high-throughput data to link gene expression profiles to the timing of an event such as death has grown, with the goal of evaluating the influence of biomedical variables on survival outcomes. One common special case in survival data is competing risks data where identifying a small subset of gene expression profiles related to cumulative incidence function (CIF) is crucial.
Methods: Several methods for directly modeling CIF are proposed, involving modeling the subdistribution hazard function of the interested cause or event using the proportional hazards approach. We proposed a regularized method for variable selection in the additive subdistribution hazards model by combining the nonconcave penalized likelihood approach and the pseudoscore method. We also conducted Monte Carlo simulations to evaluate the performance of our proposed method. In addition, a publicly available dataset was used to illustrate the proposed model.
Results: Results from simulation studies were presented together with an application to genomic data when the endpoint is progression-free survival and the objective is to identify genes related to CIF of bladder cancer in the presence of competing events. Five genes in common (CDC20, PLEK, FCN2, IGF1R and DCTD) were identified by the proposed penalized additive subdistribution hazards model with different penalties.
Conclusions: Monte Carlo simulation studies results suggested that the results of all penalties were comparable in terms of sensitivity and specificity, whereas those based on Adaptive Elastic Net (AENET) and Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO) penalties tended to perform better in terms of estimation accuracy. |
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| کلیدواژه‌های انگلیسی مقاله |
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| نویسندگان مقاله |
| Leili Tapak
Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
| Michael Kosorok
Department of Biostatistics, Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, USA
| Omid Hamidi
Department of Science, Hamedan University of Technology, Hamedan 65155, Iran
| Mahya Arayeshgari
Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
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| نشانی اینترنتی |
https://jbe.tums.ac.ir/index.php/jbe/article/view/1470 |
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