Logo PTI Logo FedCSIS

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 32

Visually Enhanced Python Functions for Clinical Equality of Measurement Assessment

,

DOI: http://dx.doi.org/10.15439/2022F32

Citation: Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 32, pages 241–249 ()

Full text

Abstract. Equivalence testing requires specific procedures usually provided by specialized statistical software. The proposed package includes customized methods to assess biomedical equivalence and focuses on translating the outcomes into visual reports. The functions are coded in an object-oriented framework, contain improved plots or novel graphs to facilitate interpretation of the results, and are accompanied by console textual outputs to support users with additional explanations. Special attention has been devoted to verifying the preliminary assumptions of the statistical tests with automatic routines. The current module covers four aspects of biomedical statistics (equivalence, Bland--Altman and ROC analyses, effect size, and confidence intervals interpretation), offering these methodologies to the biomedical community as accessible stand-alone functions. The manuscript defines software's functions and innovations with examples and theoretical explanations.

References

  1. S. C. Gad, Safety evaluation of pharmaceuticals and medical devices: international regulatory guidelines. Springer Science & Business Media, 2010.
  2. F. Home, “Orange book: approved drug products with therapeutic equivalence evaluations,” US Food Drug Adm, 2013.
  3. S.-C. Chow and S. J. Lee, “Current issues in analytical similarity assessment,” Statistics in Biopharmaceutical Research, vol. 13, no. 2, pp. 203–209, 2021. http://dx.doi.org/10.1080/19466315.2020.1801497
  4. A. Munk, J. Gene Hwang, and L. D. Brown, “Testing average equiv- alencefinding a compromise between theory and practice,” Biometrical Journal: Journal of Mathematical Methods in Biosciences, vol. 42, no. 5, pp. 531–551, 2000.
  5. J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (roc) curve.” Radiology, vol. 143, no. 1, pp. 29–36, 1982.
  6. J. M. Bland and D. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” The lancet, vol. 327, no. 8476, pp. 307–310, 1986.
  7. G. Cumming, “The new statistics: Why and how,” Psychological science, vol. 25, no. 1, pp. 7–29, 2014.
  8. C. O’Carroll, B. Rentier et al., “Evaluation of research careers fully acknowledging open science practices-rewards, incentives and/or recognition for researchers practicing open science,” Publication Office of the Europen Union, Tech. Rep., 2017.
  9. J. M. Bland and D. G. Altman, “Applying the right statistics: analyses of measurement studies,” Ultrasound in Obstetrics and Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology, vol. 22, no. 1, pp. 85–93, 2003.
  10. S.-L. Jan and G. Shieh, “The bland-altman range of agreement: Exact interval procedure and sample size determination,” Computers in Biology and Medicine, vol. 100, pp. 247–252, 2018. http://dx.doi.org/https://doi.org/10.1016/j.compbiomed.2018.06.020
  11. G. Cumming, Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. Routledge, 2013.
  12. J.-C. Goulet-Pelletier and D. Cousineau, “A review of effect sizes and their confidence intervals, part i: The cohensd family,” The Quantitative Methods for Psychology, vol. 14, no. 4, pp. 242–265, 2018.
  13. G. Shieh, “Confidence intervals and sample size calculations for the standardized mean difference effect size between two normal populations under heteroscedasticity,” Behavior research methods, vol. 45, no. 4, pp. 955–967, 2013.
  14. M. Delacre, D. Lakens, C. Ley, L. Liu, and C. Leys, “Why hedges g*s based on the non-pooled standard deviation should be reported with welchs t-test,” May 2021. [Online]. Available: psyarxiv.com/tu6mp
  15. D. Cousineau, “Approximating the distribution of cohens dp in within-subject designs,” Quant. Methods Psychol, vol. 16, pp. 418–421, 2020.
  16. Y. Shou, M. Sellbom, and H.-F. Chen, “Fundamentals of measurement in clinical psychology,” in Reference Module in Neuroscience and Biobehavioral Psychology. Elsevier, 2021. ISBN 978-0-12-809324-5
  17. Y. Tsong, X. Dong, and M. Shen, “Development of statistical methods for analytical similarity assessment,” Journal of biopharmaceutical statistics, vol. 27, no. 2, pp. 197–205, 2017.
  18. Y.-T. Weng, Y. Tsong, M. Shen, and C. Wang, “Improved wald test for equivalence assessment of analytical biosimilarity,” International Journal of Clinical Biostatistics and Biometrics, vol. 4, no. 1, pp. 1–10, 2018.
  19. G. Shieh, “Assessing agreement between two methods of quantitative measurements: Exact test procedure and sample size calculation,” Statistics in Biopharmaceutical Research, vol. 12, no. 3, pp. 352–359, 2020. http://dx.doi.org/10.1080/19466315.2019.1677495
  20. G. Shieh, S.-L. Jan, and C.-S. Leu, “Exact properties of some heteroscedastic tost alternatives for bioequivalence,” Statistics in Biopharmaceutical Research, pp. 1–10, 2021.
  21. S. Wellek, Testing statistical hypotheses of equivalence. Chapman and Hall/CRC, 2002.
  22. J. K. Kruschke and T. M. Liddell, “The bayesian new statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a bayesian perspective,” Psychonomic bulletin & review, vol. 25, no. 1, pp. 178–206, 2018.
  23. P. Martínez-Camblor, S. Pérez-Fernández, and N. Corral, “Efficient nonparametric confidence bands for receiver operating-characteristic curves,” Statistical Methods in Medical Research, vol. 27, no. 6, pp. 1892–1908, 2018.
  24. H. C. De Vet, R. W. Ostelo, Terwee et al., “Minimally important change determined by a visual method integrating an anchor-based and a distribution-based approach,” Quality of life research, vol. 16, no. 1, pp. 131–142, 2007.
  25. X. Sun and W. Xu, “Fast implementation of delongs algorithm for comparing the areas under correlated receiver operating characteristic curves,” IEEE Signal Processing Letters, vol. 21, no. 11, pp. 1389–1393, 2014.
  26. E. S. Venkatraman and C. B. Begg, “A distribution-free procedure for comparing receiver operating characteristic curves from a paired experiment,” Biometrika, vol. 83, no. 4, pp. 835–848, 1996.
  27. E. Venkatraman, “A permutation test to compare receiver operating characteristic curves,” Biometrics, vol. 56, no. 4, pp. 1134–1138, 2000.