Lab 3#

Part 1#

Write a .do file which imports transplants.dta and performs the data management/exploratory data analysis tasks. Your .do file (lab3_lastname.do) must create a log file (lab3_lastname.log). This file will contain only code and some annotation, but no answers (we will get these on our machines when we run your script). Do not submit your log files as part of the assignment.

  1. Perform a \(\chi^2\) test to test the association between recipient work-status (rec_work) and gender (gender). Using conditional if and else statements, make Stata print:

Question 1: Working for income is associated with gender, p = x.xxx

If the p-value is less than 0.05, or make Stata print:

Question 1: Working for income is NOT associated with gender, p=x.xx

If the p-value is greater than 0.05.

  1. From lab 2: Create a variable called hypertensive which is 1 for transplant recipients who have ESRD secondary to hypertension (dx==4) and 0 for everyone else. Create another variable called college which is 1 for recipients with any college, an associate’s/bachelor’s degree or a graduate degree and 0 for recipients who have no education, grade school education, or high school/GED. Create a variable called male which is 1 for male recipients and 0 for female recipients.

Run a logistic regression of hypertensive on age, BMI, college, and male gender (logistic hypertensive age bmi college male).

Write a program called table_loop that is an updated version of your program called table_q6 from Lab 2. table_loop should use a foreach loop to create the display of odds ratios, like this:

Regression table (Question 2)

age        x.xx (x.xx - x.xx)
bmi        x.xx (x.xx - x.xx)
college    x.xx (x.xx - x.xx)
male       x.xx (x.xx - x.xx)

Lab 3 Part 2

  1. Write a program called table_varlist that is an updated version of table_loop from question 2. table_varlist should incorporate syntax varlist and a foreach loop to create the display of odds ratios. So if we run:

 table_varlist age male bmi college

We should get the following table:

Regression table (Question 3)

age        x.xx (x.xx - x.xx)
male       x.xx (x.xx - x.xx)
bmi        x.xx (x.xx - x.xx)
college    x.xx (x.xx - x.xx)

except fill in the correct numbers.

  1. Create a variable called tall that is 1 if the recipient’s height (rec_hgt_cm) is greater than or equal to the 75th percentile of height of all recipients in the dataset. (Hint: how can you use summarize and macros to generate a variable based on the mean of another variable?) tall should be equal to 0 for recipients with height less than the overall 75th percentile for height. Create a variable called white that is 1 if the recipient is white and 0 for all other races. Create a variable called age_10y which is age in decades (age/10). You will need to create variable labels for new variables tall, white, and age_10y, and you can change existing variable labels to improve readability of the table as well.

Run a logistic regression of tall on age, weight (rec_wgt_kg), gender, and white race:

logistic tall age rec_wgt_kg gender white

Use putexcel to create a table in Excel that displays the odds ratios:

Variable            OR (95% CI)
___________________________________
Age (per 10y)       X.XX (X.XX-X.XX)
Weight in kg        X.XX (X.XX-X.XX)
Female Gender       X.XX (X.XX-X.XX)
White vs Non-white  X.XX (X.XX-X.XX)