AE 18: Response to Leukemia Treatment

Announcements

Tea with a TA!

Hang out with the TAs from STA 210! This is a casual conversation and a fun opportunity to meet the members of the STA 210 teaching team. The only rule is these can’t turn into office hours!

Tea with a TA counts as a statistics experience.

Emily , Thu, Oct 15, 11a - 12p
- Click here to sign up. Zoom details will be emailed before the event.

This is Statistics Fall Data Challenge

Click here for details on the Get out the Vote! Fall Data Challenge by the American Statistical Association (ASA). Submissions are due November 11.

Questions from the video?

Response to Leukemia treatment

Today’s data is from a study where 51 untreated adult patients with acute myeloblastic leukemia who were given a course of treatment, and they were assessed as to their response to the treatment.¹

The goal of today’s analysis is to use pre-treatment to predict how likely it is a patient will respond to the treatment.

We will use the following variables:

Age: Age at diagnosis (in years)
Smear: Differential percentage of blasts
Infil: Percentage of absolute marrow leukemia infiltrate
Index: Percentage labeling index of the bone marrow leukemia cells
Blasts: Absolute number of blasts, in thousands
Temp: Highest temperature of the patient prior to treatment, in degrees Fahrenheit
Resp: 1 = responded to treatment or 0 = failed to respond

Part 1: Age

Fit a model using Age to predict whether or not a patient responded to the treatment. You will need to make Resp a factor before fitting the model.
Interpret the coefficient of Age in the context of the data.
What is the distribution of the test statistic associated with Age?
What is the conclusion in the context of the data?

Part 2: All pre-treatment variables

Now let’s consider all pre-treatment variables: Age, Smear, Infil, Index, Blasts and Temp. Fit a model using these six variables to predict whether a patient responded to the treatment.
Based on the model, which pre-treatment variables are statistically significant? What does it mean for these variables to be “statistically significant”?
Let’s see if a model that only includes the significant predictors is a reasonable choice for the final model. Use a drop-in-deviance test to compare a model that includes only the significant predictors to the full model from Ex 5. Based on the results of this test, which model do you choose as the final model?