Sample Size for Measures of Association
Correlation, Agreement and Diagnostic Measures
Evaluating the relationship between variables is one of the most common goals of statistical analysis.
You will learn about:
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Measures of Association Overview
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Correlation Methods and Sample Size (Spearman’s Correlation)
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Agreement Methods and Sample Size (Cohen’s Kappa for Agreement)
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Agreement Methods and Sample Size (AUC from ROC Analysis)
Sample Size for Measures of Association
Correlation measures are widely used to summarise the strength of association between variables.
Commonly seen in areas such as regression analysis, the most widely used version is the Pearson correlation for a linear relationship.
However other correlations may be more suitable in certain contexts such as rank correlations like Spearman’s correlation for dealing with ordinal rank data.
Assessing the reliability of different “raters” is vital in areas where multiple assessors criteria or methods are available to evaluate a disease or condition.
Cohen’s Kappa statistic is a widely used approach to quantify the degree of agreement between multiple raters and provides a basis for the testing and estimation of interrater reliability.
The statistical evaluation of diagnostic testing is a vital component of ensuring that proposed screening or testing procedures have the appropriate accuracy for clinical usage.
A plethora of measures exist to evaluate the performance of a diagnostic test but one of the most common is Receiver Operating Characteristic (ROC) curve analysis where the Area Under the Curve (AUC) provides a snapshot of how well a test performs over the entire range of discrimination boundaries.
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