Few basic Epidemiology terminologies
- 1. Epidemiology Terminologies Dr Venkatesh Karthikeyan 03/10/2021 www.drvenkateshkarthikeyan.com
- 2. • Accuracy • Repeatability • Validity • Sensitivity • Specificity • AUC ROC curve • Kappa statistics • Correlation
- 3. 1 2 3 4 5 Validity Reliability Accuracy Sensitivity Specificity Basic requirements of measurements
- 4. Basic requirements of measurements Accuracy • It refers to the closeness with which measured value agree with “true” values.
- 5. Basic requirements of measurements Repeatability • Sometimes called reliability, precision or reproducibility • The test must give consistent results when repeated more than once on the same individual or material
- 6. Basic requirements of measurements Validity • Validity refers to what extent the test accurately measures which it purports to measure. • Validity expresses the ability of a test to separate or distinguish those who have the disease from those who do not. • E.g: Glycosuria is a useful screening test for diabetes, but a more valid or accurate test is glucose tolerance test
- 7. Basic requirements of measurements Components of validity • Sensitivity • Ability of a test to identify correctly all those who have the disease. • i.e., ability to identify “True positive” • Specificity • Ability of a test to identify correctly those who do not have the disease • i.e., ability to identify “True negative”
- 8. Basic requirements of measurements Components of validity Sensitivity and Specificity Screening test results Diseased Not diseased Total Positive a (True positive) b (False positive) a+b Negative c (False negative) d (True negative) c+d Total a+c b+d a+b+c+d •Sensitivity = a/(a+c) * 100 •Specificity = d/(b+d) * 100 •Positive predictive value = a/(a+b) * 100 •Negative predictive value = d/(c+d) * 100
- 9. AUC – ROC curve • The Receiver Operator Characteristic (ROC) curve is an evaluation metric for binary classification problems. • It is a probability curve that plots the TPR against FPR at various threshold values • The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. • The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.
- 10. When AUC = 1, then the classifier is able to perfectly distinguish between all the Positive and the Negative class points correctly. If, however, the AUC had been 0, then the classifier would be predicting all Negatives as Positives, and all Positives as Negatives.
- 11. When 0.5<AUC<1, there is a high chance that the classifier will be able to distinguish the positive class values from the negative class values. This is so because the classifier is able to detect more numbers of True positives and True negatives than False negatives and False positives.
- 12. • When AUC=0.5, then the classifier is not able to distinguish between Positive and Negative class points. Meaning either the classifier is predicting random class or constant class for all the data points.
- 13. • So, the higher the AUC value for a classifier, the better its ability to distinguish between positive and negative classes.
- 14. Kappa statistics
- 15. • The kappa statistic is frequently used to test interrater reliability. • The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. • Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability.
- 16. • While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. • kappa can range from −1 to +1 • Cohen’s suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable
- 17. Correlation
- 18. • Correlation is a statistical method used to assess a possible linear association between two continuous variables. • Correlation is used to refer to an association, connection, or any form of relationship, link or correspondence. • Correlation is measured by a statistic called the correlation coefficient, which represents the strength of the putative linear association between the variables in question.
- 19. • A correlation coefficient of zero indicates that no linear relationship exists between two continuous variables, and a correlation coefficient of −1 or +1 indicates a perfect linear relationship. • The strength of relationship can be anywhere between −1 and +1. • The stronger the correlation, the closer the correlation coefficient comes to ±1.
- 20. • Correlation coefficients are used to assess the strength and direction of the linear relationships between pairs of variables. • When both variables are normally distributed use Pearson's correlation coefficient, otherwise use Spearman's correlation coefficient.
- 21. • Correlation coefficients do not communicate information about whether one variable moves in response to another. • There is no attempt to establish one variable as dependent and the other as independent. • Thus, relationships identified using correlation coefficients should be interpreted for what they are: associations, not causal relationships.