#d <- read.csv("data/bivalirudin.csv") # load data
#table_dosechgs_gender <- xtabs(~d$d_Male + d$DV_3DoseChanges, data=d) # crosstabulate
#knitr::kable(table_dosechgs_gender, align = "l")
#summary(table_dosechgs_gender) # calculate chi-squareStatistics for bivariate analyses
Here are two statistics for bivariate (two variable) analyses:
Chi-square \(\chi 2\) tests of association
The chi-square \(\chi 2\) test is a commonly used statistic for nominal/categorical data. We use it to examine the distribution of cases across categories. Essentially, it compares the distribution of cases you actually see to the distribution of cases you would expect to see from normal variation.
Here is one example of a chi-square \(\chi 2\) test for a recent resident project. The question is whether gender (male/female) makes a statistically significant difference in whether patients need three or more dose changes of bivalirudin before they reach a therapeutic goal.
The chi-square \(\chi 2\) value of 1.9421 with one degree of freedom has a p-value of 0.1634. It is not statistically significant, suggesting that gender makes no significant difference in reaching therapeutic goal.
t tests
The t test is a commonly used statistic for comparing two groups on a continuous outcome.