An Application of Spatially Adjusted Kolmogorov-smirnov Test in Comparing Heart PET Scans

Authors

  • Wenjun Zheng School of Public Health, The University of Texas
  • Dejian Lai School of Public Health, The University of Texas
  • Zhe Wang Human Genetics Center, The University of Texas
  • Nils P. Johnson Weatherhead PET Imaging Center, The University of Texas
  • K. Lance Gould Weatherhead PET Imaging Center, The University of Texas

DOI:

https://doi.org/10.26398/IJAS.0032-014

Keywords:

Kolmogorov-Smirnov, Cardiovascular disease, PET, Spatial autocorrelation

Abstract

The Kolmogorov-Smirnov (KS) test has been popular in many applied fields. Published research has suggested the utility of the KS test in image processing, histogram analysis, and PET/CT scans. However, the fundamental assumption of independence in a statistical model is easily overlooked. When the KS test is applied to spatial analysis, autocorrelation may cause the KS test to have an inflated type I error (small p-values) if no adjustments for spatial correlation are applied. To adjust for autocorrelation, the KS test must incorporate spatial adjustment. The spatially-adjusted KS has a controlled type I error and non-inferior power compared to the original KS test. Utilizing the KS test with spatial adjustment, we reanalyzed a trial comparing two types of stress medications: regadenoson (administered using different timings) versus dipyridamole. To analyze the PET scans with spatial autocorrelation, we introduced a novel way of reconstructing the shape of the human heart using spherical coordinates, and compared the KS test with spatial adjustment to a KS test with adjustment for correlation. The results showed that the reconstructed PET scans analyzed by the KS test with spatial adjustment have

controlled p-values.

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Published

2021-04-02

How to Cite

Zheng, W. ., Lai, D. ., Wang, Z. ., Johnson, N. P. ., & Gould, K. L. . (2021). An Application of Spatially Adjusted Kolmogorov-smirnov Test in Comparing Heart PET Scans. Statistica Applicata - Italian Journal of Applied Statistics, 32(3), 249–267. https://doi.org/10.26398/IJAS.0032-014

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