Clinical laboratory medicine measurements correlation analysis under uncertainty

Abstract

Background: The Spearman rank correlation test under classical statistics cannot be applied when the paired data is in interval or indeterminacy is presented in the paired data. Methods: In this paper, the Spearman rank correlation test under neutrosophic statistics will be introduced. The proposed Spearman rank correlation test will be a generalization of the existing Spearman rank correlation test. Results: The proposed test is supposed to be more informative, flexible, and adequate to apply for the analysis of the measurement data. The application of the proposed test is given using the measurement of luteotropichormone data obtained from the clinical laboratory. Based on the information, the probability of accepting the null hypothesis (Formula presented.) is 0.95, the chance of committing a type-I error is 0.05 and the chance of indeterminacy about the acceptance of (Formula presented.) is 69%. Conclusions: From the analysis, it is noted that the proposed test is more efficient in terms of the measure of indeterminacy as compared with the existing test. From the study, it is concluded that the proposed test is more informative, applicable and useable under an indeterminate environment as compared with the existing test under classical statistics. Therefore, it is recommended to apply the proposed test in clinical laboratories for testing the correlation between instruments.