AE 19: Response to Leukemia Treatment

Prediction

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Big data and public policy event on Oct 26

Final project - Draft due Oct 28

• Write the draft in the written-report.Rmd file in the project repo.
• Draft should include
  • exploratory data analysis
  • initial model selection (main effects + interactions)
  • initial interpretations / conclusions from model

Quiz 02

• Question 1.5
• Question 2
• Question 3 - D and E

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

1. Fit a model using Age, Index and Temp to predict the odds a patient will respond to the treatment.

2. Use the augment function to obtain the predicted probability each patient in the data set will respond to the treatment.

3. Construct a confusion matrix using 0.5 as the decision-making threshold.

4. Use your confusion matrix from the previous exercise to calculate the following:

• Sensitivity
• Specificity
• Misclassification rate

5. Create the ROC curve for this model.

6. What is the area under the curve? Is this model a good fit for the data?

7. Suppose a doctor wants to use your model to determine if she should recommend this particular treatment for her patients with leukemia. Based on the data from the ROC curve, what decision-making threshold do you recommend the doctor use to select patients for this treatment? What factors did you consider to determine the threshold?

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1. The data set is from the Stat2Data R package↩︎