Lab 2#

Part 1#

Write a .do file which imports transplants.dta and performs the data management/exploratory data analysis tasks described below using the slides we have discussed in class. Your .do file (lab1_lastname.do) must create a log file (lab1_lastname.log). This file will contain your answers for both part 1 and part 2 of today’s lab. Your .do file should follow conventions for .do file structure described in class. Do not submit your log files as part of the assignment.

  1. Do a \(\chi^2\) test to test the association between recipient HCV status (rec_hcv) and gender (gender). Make Stata print the p value, like this:

Chi-squared p value (question 1): xxxxxx

Except print the correct p value.

Note: here and in other questions, if the p value is (for example) 0.4, don't have a command like

disp 0.4

Instead, write code to calculate and then display the number we are asking for.

  1. Do a one-sided Fisher exact test to test the association between recipient HCV status (rec_hcv) and gender (gender). Make Stata print the one-sided p value, like this:

One-sided Fisher exact p value (question 2): xxxxxx

Except print the correct p value.

  1. Run a logistic regression to test the association between age and recipient HCV status (logistic rec_hcv age). Make Stata print the odds ratio from that regression, like this:

Odds ratio (question 3): xx.xx (95% CI yy.yy-zz.zz)

  1. Make Stata print the p value for the coefficient from that regression, like this:

P value (question 4): 0.yyyy

Part 2#

  1. Write code to produce the following output:

Question 5: mean peak PRA is 18.4 in males and 14.5 in females

  1. 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 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.

Now run a logistic regression of hypertensive on age, BMI, college, and male gender (logistic hypertensive age bmi college male). Write a program called table_q6 that displays a table of odds ratios, like this:

Regression table (Question 6)
 
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)

except fill in the correct numbers.