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CC BY-NC-ND 4.0 · Indian J Radiol Imaging 2025; 35(S 01): S74-S92
DOI: 10.1055/s-0044-1800971
Research and Publications: The Process
Review Article

Statistics Primer for Radiologists: Part 2—Advanced statistics for Enhancing Diagnostic Precision and Research Validity

Authors

  • Adarsh Anil Kumar

    1   Department of Imaging Sciences and Interventional Radiology, Sree Chitra Institute of Medical Sciences, Trivandrum, Kerala, India

  • Santhosh Kannath

    1   Department of Imaging Sciences and Interventional Radiology, Sree Chitra Institute of Medical Sciences, Trivandrum, Kerala, India

  • Jineesh Valakkada 

    1   Department of Imaging Sciences and Interventional Radiology, Sree Chitra Institute of Medical Sciences, Trivandrum, Kerala, India

Funding None.
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Abstract

Second part of this statistics primer focuses on advanced statistical concepts continuing on the foundation of basic statistics built from the first part of this primer. This advanced primer aims to delve deeper into essential statistical concepts beyond the basics, equipping the reader with the knowledge to effectively analyze complex data sets, explore correlations and causality, employ regression analysis techniques, interpret survival curves, and evaluate diagnostic tests rigorously. It primarily focuses on the statistical tests used to analyze the relationship between groups of variables (the statistical tests to analyze the difference between groups of variables was discussed in the part 1 of this series). Toward the end of the article concepts of survival curves and methods for assessing the diagnostic accuracy of tests are stressed upon.

Keywords

primer - radiologists - statistics

Note

Work done in: Department of Imaging Sciences and Interventional Radiology, Sree Chitra Institute of Medical Sciences, Trivandrum


Authors' Contributions

All the authors were involved in the procedure, data collection, and manuscript revision.




Publication History

Article published online:
09 January 2025

© 2025. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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