Chapter 13

Comparing Proportions and Analyzing Cross-Tabulations

In This Chapter

arrow Testing for association between categorical variables with the Pearson chi-square and Fisher Exact tests

arrow Adjusting for confounders with the Mantel-Haenszel test for stratified fourfold tables

arrow Spotting trends across ordinal (sequenced) categories with the Kendall tau test

arrow Estimating sample sizes for tests of association

Suppose you’re conducting a clinical trial of a new treatment for an acute disease with a high mortality rate, for which no effective treatment currently exists. You study 100 consecutive subjects with this condition and randomly assign 60 of them to receive the new treatment and 40 to receive a placebo or sham treatment. Then you record whether each subject lives or dies. Your data file has two dichotomous categorical variables: the treatment group (drug or placebo) and the outcome (lives or dies).

You find that 30 of the 40 untreated (placebo) subjects died (a 75 percent mortality rate), while only 27 of the 60 treated subjects died (45 percent mortality). The drug appears to reduce ...

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