SMHS AssociationTests

Scientific Methods for Health Sciences - Association Tests

Overview

Measuring the association between two quantities is one of the most commonly applied tools researchers needed in studies. The term association implies on the possible correlation where two or more variables vary accordingly to some pattern. There are many statistical measures of association including relative ratio, odds ratio and absolute risk reduction. In this section, we are going to introduce measures of association in different studies.

Motivation

In many cases, we need to measure if two quantities are associated with each other -- that is if two or more variables vary together according to some pattern. There are many statistical tools we can apply to measure the association between variables. How can we decide what types of measures we need to use? How do we interpret the test results? What does the test results imply about the association between the variables we studied?

Theory

• Measures of Association: (1) relative measures $Relative\, risk=\frac{Cumulative\, incidence\, in\, exposed}{Cumulative\,incidence\, in \,unexposed}=ratio\, of\, risks =Risk\, Ratio$;

$Rate \,Ratio=\frac{Incidence\, rate\, in\, exposed} {Incidence\, rate\, in\, unexposed}$;

(2) difference: $Efficacy=\frac{Cumulative\, incidence\, in \,placebo \,- \,Cumulative\, incidence\, in\, the\, treatement} {Cumulative\, incidence\, rate\, in\, placebo\, group}.

We are going to interpret the measurement results and conclude about the association between variables through examples in different types of trials.

• Chi-square test: a non-parametric test of statistical significance of two variables. It tests if the measured factor is associated with the members in one of two samples with chi square. For example, the chi-square test tests whether there is statistical evidence that the measured factor is not randomly distributed in the cases compared to the controls in a case-control study. The test statistic is 〖χ_o〗^2=∑_(i=1)^n▒(O_i-E_i )^2/E_i ~χ_df^2, where E_i is the expected frequency under the null hypothesis and O_i is the observed frequency, n is the number of cells in the table and df=(# rows-1)(# columns-1), E=(row total*column total)/(gran total). The null hypothesis is that there is no association between exposure group the disease studied.
  • Conditions for validity of the χ^2test are:
    • Design conditions
      • for a goodness of fit, it must be reasonable to regard the data as a random sample of categorical observations from a large population.
      • for a contingency table, it must be appropriate to view the data in one of the following ways: as two or more independent random samples, observed with respect to a categorical variable; as one random sample, observed with respect to two categorical variables.
      • for either type of test, the observations within a sample must be independent of one another.
    • Sample conditions: critical values only work if each expected value > 5
• Example of association: a study on the association of a particular gene and the risk of late onset disease. The data is summarized in the data table below:

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• SOCR Home page: http://www.socr.umich.edu

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