Creating 2 variables from one for paired ttest

[Banke]

ID	sex	diasbp	male_diasbp	female_diasbp
1	1	45	45	.
2	1	54	54	.
3	2	65	.	65
4	2	68	.	68

data biostat.original;
input id sex diasp;
data lines
1  1  65
2  2  45  
3  2  78
4  2  50
5  1  49

data biostat.male_dbp biostat.female_dbp;
         set biostat.dig200;
      if sex = 1 then output biostat.male_dbp;
else output biostat.female_dbp;
run;


data biostat.male_dbp1;
      set biostat.male_dbp;
  male_diabp = diabp;
run;

data biostat.female_dbp1;
     set biostat.female_dbp;
  female_diabp = diabp;
run;

proc sort data = biostat.male_dbp1;
   by ID;
run;

proc sort data = biostat.female_dbp1;
   by ID;
run;

data biostat.dbp_grps;
    merge biostat.male_dbp1 biostat.female_dbp1;
by ID;
run;

proc ttest;
paired male_diabp*female_diabp;
run;

Hello everyone, here again😊

i want to conduct an hypothesis test on the mean difference between male and female diastolic blood pressures (diabp). This would require a paired ttest, hence 2 paired variables considering the syntax...

I have the diabp in just one variable and there is another variable for sex (coded 1 and 2 for male and female respectively), to get malediabp and femalediabp into 2 different variables , i first output each of them into different datasets and merged by a common variable (ID). The step is however long, i would please like to know if there is a more efficient way of doing this. Thanks

[PaigeMiller]

I have to disagree, if you want to test the mean diastolic blood pressure difference between males and females, you want an UNpaired t-test.

A paired t-test in this case does not make sense and does not fit the experiment.

[Banke]

Oops! You are right, there is no pairing between them, they are independent of each other. Thanks

[ballardw]

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@Banke wrote:
Oops! You are right, there is no pairing between them, they are independent of each other. Thanks

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Unless you have that many sex-change operations in your subject pool...

[Reeza]

Please fix your code before posting. The first two steps do not work when tested.

As also indicated by PaigeMiller, given what you've posted here this does not align with a paired t-test. A paired t-test is usually used for repeated measurements on the same individual. In this case you have data on different individuals and should run a standard t-test. If you do want to restructure your data to a wide format for a paired t-test though you should look into PROC TRANSPOSE.

Transposing data tutorials:
Long to Wide:
https://stats.idre.ucla.edu/sas/modules/how-to-reshape-data-long-to-wide-using-proc-transpose/

https://stats.idre.ucla.edu/sas/modules/reshaping-data-long-to-wide-using-the-data-step/

Wide to Long:
https://stats.idre.ucla.edu/sas/modules/how-to-reshape-data-wide-to-long-using-proc-transpose/

https://stats.idre.ucla.edu/sas/modules/reshaping-data-wide-to-long-using-a-data-step/

And sometimes a double transpose is needed for extra wide data sets:
https://gist.github.com/statgeek/2321b6f62ab78d5bf2b0a5a8626bd7cd

---

@Banke wrote:

ID	sex	diasbp	male_diasbp	female_diasbp
1	1	45	45	.
2	1	54	54	.
3	2	65	.	65
4	2	68	.	68

data biostat.original;
input id sex diasp;
data lines
1  1  65
2  2  45  
3  2  78
4  2  50
5  1  49

data biostat.male_dbp biostat.female_dbp;
         set biostat.dig200;
      if sex = 1 then output biostat.male_dbp;
else output biostat.female_dbp;
run;


data biostat.male_dbp1;
      set biostat.male_dbp;
  male_diabp = diabp;
run;

data biostat.female_dbp1;
     set biostat.female_dbp;
  female_diabp = diabp;
run;

proc sort data = biostat.male_dbp1;
   by ID;
run;

proc sort data = biostat.female_dbp1;
   by ID;
run;

data biostat.dbp_grps;
    merge biostat.male_dbp1 biostat.female_dbp1;
by ID;
run;

proc ttest;
paired male_diabp*female_diabp;
run;

Hello everyone, here again😊

i want to conduct an hypothesis test on the mean difference between male and female diastolic blood pressures (diabp). This would require a paired ttest, hence 2 paired variables considering the syntax...

I have the diabp in just one variable and there is another variable for sex (coded 1 and 2 for male and female respectively), to get malediabp and femalediabp into 2 different variables , i first output each of them into different datasets and merged by a common variable (ID). The step is however long, i would please like to know if there is a more efficient way of doing this. Thanks

---