Lab 1#

Part 1#

Write Stata commands to solve the following questions. Keep them in a .txt or .docx file. In the second half of today’s class you will learn how to put them in a .do file or script to run all of the commands at the same time. For now, put them in a text file and copy & paste one line at a time into Stata to get the answers.

You will turn this work in together with Lab 1 Part 2.

Write Stata commands to answer the following questions about the dataset transplants.dta:

  1. How many total patients are in the dataset? 2000

  2. The variable dx represents diagnosis and age represents patient age. How many patients were between the ages of 50 and 60 and had diabetes as their primary diagnosis at the time of transplant? 29

  3. The variable wait_yrs represents the amount of time a patient spent on the transplant waitlist prior to transplant. How many patients waited longer than 5 years for a transplant? 214

  4. What is the age, race (variable: race), and Hepatitis C status (variable: rec_hcv_antibody) of patient with ID=493? 75/white/negative

  5. What are the mean ages at transplant for male and female recipients? (Hint: consider tab, sum) (Variable: gender 0=Male, 1 = Female). 50.96; 49.52

Part 2#

  1. Generate a variable called age_categories that is 0 for children (ages<18), 1 for adults (18-60), and 2 for senior citizens (>60). What is the mean number of years patients spent on the waitlist in each age group? (Hint: consider tab, sum) 0.71y; 2.48y; 2.27y

  2. Type the command describe to see a list of all variables. Which ones don’t have a variable label? Write code to give a variable label to all variables that don’t yet have one. Then run describe again to show the labeled variables. Any description is fine

  3. Variable abo represents blood type and is coded as 1=A, 2=B, 3=AB, 4=O. Variable prev represents whether the patient has had a previous transplant and is coded 0=No, 1=Yes. Create a value label for prev. Put the command tab abo prev in your script, to produce the following output (with xxxx filled in):

. tab abo prev 

           |    Prev Kidney Tx
       abo |         No       Yes |     Total
-----------+----------------------+----------
       1=A |       613        107 |       720 
       2=B |       226         23 |       249 
      3=AB |       104         12 |       116 
       4=O |       800        115 |       915 
-----------+----------------------+----------
     Total |     1,743        257 |     2,000

.

  1. Generate a new variable called bmi_whocat, representing WHO categories of BMI. BMI category should have the following values: missing if BMI is missing; 1 (labeled “underweight”) if BMI<18.5; 2 (labeled “normal”) if BMI is 18.5-25; 3 (labeled “overweight”) if BMI is 25.1-30; 4 (labeled “obese”) if BMI is 30.1=40.

. tab bmi_whocat

 bmi_whocat |      Freq.     Percent        Cum.
------------+-----------------------------------
underweight |         51        2.77        2.77
     normal |        629       34.13       36.90
 overweight |        632       34.29       71.19
      obese |        531       28.81      100.00
------------+-----------------------------------
      Total |      1,843      100.00

.

  1. List the ID and age of every patient who is older than 80. List them in order with the oldest patient first.

. gsort -age

. list fake_id age if age>80, clean noobs

       1535    85  
       1636    84  
       1592    84  
        736    82  
       1527    82  
        911    82  
        647    81  
       1061    81  
       1435    81   

.

qui {
   *10 pts per q; 100 max
   cls
   use transplants, clear //10pts   
   noi count //q1, 10pts     
   noi count if inrange(age,50,60)&dx==2 //q2, 10pts 
   noi count if inrange(wait_yrs,5,100) //q3, 10pts
   noi list age race rec_hcv_antibody if fake_id==493 //q4, 10pts
   if 5 {  
       egen sum_age = mean(age), by(gender) //4pts
       egen tag_gender = tag(gender) //3pts
       noi list sum_age if tag_gender //3pts
   }
   if 6 {  
	   g age_categories=0 if inrange(age,0,17) //3pts, alternative1
	   replace age_categories=1 if inrange(age,18,60) //2pts
	   replace age_categories=2 if inrange(age,61,100) //2pts
	   noi tab age_categories, sum(wait_yrs) //3pts
	   egen mean_wait_yrs = mean(wait_yrs), by(age_categories) //alternative2
	   forval i=1/3 {
          noi di mean_wait_yrs[`i'] //order different; starts with 1, ends with 0
	   }
	   
   }
   if 7 {  
   	   desc //1pt
	   lab var fake_id "Unique ID" //1pt
	   lab var ctr_id "Center ID" //1pt 
	   lab var end_date "End Date" //1pt
	   lab var died "Died" //1pt 
	   lab var tx_failed "Transplant Failed" //1pt
	   lab var wait_yrs "Years on the waiting list" //1pt
	   lab var gender "Gender" //1pt
	   noi desc //2pt
   }
   if 8 {  
       label define Prev 0 "No" 1 "Yes" //3pts 
   	   label values prev Prev //4pts
	   noi tab abo prev //3pts
   }
   if 9 {  
	   g bmi_whocat=1 if inrange(bmi,0,18.5) //2pt
	   replace bmi_whocat=2 if inrange(bmi,18.5,25) //1pt
	   replace bmi_whocat=3 if inrange(bmi,25.1,30) //1pt
	   replace bmi_whocat=4 if inrange(bmi,30.1,100) //1pt
	   label define Bmi_whocat 1 "underweight" 2 "normal" ///
	      3 "overweight" 4 "obese" //2pt
	   label values bmi_whocat Bmi_whocat //2pt
       noi tab abo prev //1pt
   }
   if 10 {
	   gsort -age //5pts: 1 for age; 1 for -; 1 for sort; 2 gsort  
	   noi list fake_id age if age>80, clean noobs 
	   //5pts: 2 for list; 1 for fake_id; 1 for age; 1 for if age>80
   }
}