# Lesson 18: Correlation and Agreement

### Introduction

Many biostatistical analyses are conducted to study the relationship between two continuous or ordinal scale variables within a group of patients.

Purposes of these analyses include:

- assessing correlation between the two variables, i.e., identifying whether values of one variable tend to be higher (or possibly lower) for higher values of the other variable;
- assessing the amount of agreement between the values of the two variables, i.e., comparing alternative ways of measuring or assessing the same response;
- assessing the ability of one variable to predict values of the other variable, i.e., formulating predictive models via regression analyses.

This lesson will focus only on correlation and agreement, (issues numbered 1 and 2 listed above).

### Learning objectives & outcomes

Upon completion of this lesson, you should be able to do the following:

- Recognize appropriate use of Pearson correlation, Spearman correlation, Kendall’s tau-b and Cohen’s Kappa statistics.
- Use a SAS program to produce confidence intervals for correlation coefficients and interpret the results.
- Adapt a SAS program to produce the correlation coefficients, their confidence intervals and Kendall’s tau-b.
- Recognize situations that call for the use of a statistic measuring concordance.
- Distinguish between a concordance correlation coefficient and a Kappa statistic based on the type of data used for each.
- Interpret a concordance correlation coefficient and a Kappa statistic.