During week 9 , we looked at a number of combinations of logistic regression, different types of explanatory variables. For this example, let’s look at a dataset where our outcome or response variable has more than 2 levels, a proportional odds model. The coding we used with Proc Logistic is the same you would use here.
This example has males and females, a number of them have been placed on an active trial whereas others have been placed on a placebo trial. The outcome variable is called improve and has been classified into 3 levels: marked improvement, some improvement, or no improvement. For this example I will provide you with the raw data, you will need to do “something” to the data before analyzing it.
| gender | treatment | improve | count |
| female | active | marked | 16 |
| female | active | some | 5 |
| female | active | none | 6 |
| female | placebo | marked | 6 |
| female | placebo | some | 7 |
| female | placebo | none | 19 |
| male | active | marked | 5 |
| male | active | some | 2 |
| male | active | none | 7 |
| male | placebo | marked | 1 |
| male | placebo | some | 0 |
| male | placebo | none | 10 |
Is there a different in improvement for individuals on active vs placebo and is there a gender effect?
SASsy Fridays 