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[A SAS marco program for batch processing of univariate Cox regression analysis for great database].

Rendong Yang1, Jie Xiong, Yangqin Peng

  • 1Department of Epidemiology and Health Statistics, School of Public Health, Central South University, Changsha 410078, China.

Zhong Nan Da Xue Xue Bao. Yi Xue Ban = Journal of Central South University. Medical Sciences
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A SAS macro program automates univariate Cox regression analysis for large datasets. This tool efficiently processes survival data, aiding in the identification of significant RNA molecules for ovarian cancer research.

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Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Univariate Cox regression analysis is crucial for identifying survival-associated factors in large biological databases.
  • Manual batch processing of such analyses is time-consuming and prone to errors.
  • Efficient computational tools are needed to streamline statistical analysis in genomics research.

Purpose of the Study:

  • To develop a SAS macro program for automated batch processing of univariate Cox regression.
  • To facilitate the analysis of large datasets for identifying survival-correlated biomarkers.
  • To apply the program for screening RNA molecules associated with ovarian cancer survival.

Main Methods:

  • A SAS macro program was developed using SAS 9.2.
  • The program integrates data filtering, Cox regression analysis, and P-value export to Excel.
  • The macro was applied to a dataset for identifying survival-related RNA molecules in ovarian cancer.

Main Results:

  • The SAS macro program successfully performed batch processing of univariate Cox regression analyses.
  • Automated selection and export of significant results were achieved.
  • The program demonstrated efficiency in handling large-scale survival data analysis.

Conclusions:

  • The developed SAS macro program effectively automates univariate Cox regression analysis.
  • This tool has significant potential to reduce the workload in statistical analysis.
  • It provides a robust basis for batch processing of survival analyses in large databases.