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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
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Unpaired data empowers association tests.

Mingming Gong1,2,3, Peng Liu1, Frank C Sciurba1

  • 1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA.

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|October 18, 2020
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Summary
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A new Semi-paired Association Test (SAT) method leverages both paired and unpaired data from electronic health records (EHRs) to improve biomarker association discovery. This approach enhances statistical power and controls false discoveries in biomedical research.

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Area of Science:

  • Biomedical research
  • Health informatics
  • Biomarker discovery

Background:

  • Biomedical research increasingly uses retrospective data from healthcare systems to understand disease associations.
  • Analyzing associations between diverse data types (genetic, blood biomarkers, imaging) offers a holistic view of human diseases.
  • Current methods for testing associations in Electronic Health Records (EHRs) require individuals with complete data, limiting sample size and statistical power.

Purpose of the Study:

  • To introduce a novel statistical method, the Semi-paired Association Test (SAT), for analyzing biomarker associations in EHRs.
  • To enhance the power of association tests by incorporating both paired and unpaired data.
  • To improve the control of false discoveries compared to classical approaches.

Main Methods:

  • Developed the Semi-paired Association Test (SAT) to utilize both paired and unpaired data from EHRs.
  • Conducted theoretical and empirical studies, including simulations, to evaluate SAT's properties.
  • Applied SAT to real-world data in the context of Chronic Obstructive Pulmonary Disease (COPD).

Main Results:

  • The SAT method demonstrated improved control of false discovery rates and increased statistical power.
  • Application to COPD data revealed significant associations between CT image features and blood biomarkers.
  • Identified associations between imaging data and the expression of numerous immune system-related genes.

Conclusions:

  • The SAT method offers a powerful new approach for biomarker association studies using EHR data.
  • Incorporating unpaired data significantly enhances the ability to detect true associations.
  • The findings highlight potential links between imaging phenotypes, blood biomarkers, and immune gene expression in COPD.