Hello,
I would like to use the Missing option in the different classes below.
I want Age/Sex/Race/Insurance/Underly (Missing value inclusion) and Oxy/ICU/Incub/Ecmo/Died(Missing value exclusion). In addition, is there a way to get the SUM of each class in the Tabulate statement too? Please guild me a way to do it. Thanks.
proc tabulate data=ARI_UC_Elg_IDs;
class Age Sex race Insurance oxy ICU Intub Ecmo Died Underly;
tables (Age Sex race Insurance oxy ICU Intub Ecmo Died)*(n colpctn*f=4.2), Underly;
where Age_group in (1,2,3);
run;
You can have multiple CLASS statements, so to control MISSING option you can do:
proc tabulate data=ARI_UC_Elg_IDs;
class Age Sex race Insurance Underly / missing ;
class oxy ICU Intub Ecmo Died ;
tables (Age Sex race Insurance oxy ICU Intub Ecmo Died)*(n colpctn*f=4.2), Underly;
where Age_group in (1,2,3);
run;
There are examples in the docs about getting a sum. You need to use a VAR statement to add an analytical variable. See e.g. : https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/proc/p13k5zc709o4t3n1k9u2s7dsnzv9.htm
You can have multiple CLASS statements, so to control MISSING option you can do:
proc tabulate data=ARI_UC_Elg_IDs;
class Age Sex race Insurance Underly / missing ;
class oxy ICU Intub Ecmo Died ;
tables (Age Sex race Insurance oxy ICU Intub Ecmo Died)*(n colpctn*f=4.2), Underly;
where Age_group in (1,2,3);
run;
There are examples in the docs about getting a sum. You need to use a VAR statement to add an analytical variable. See e.g. : https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/proc/p13k5zc709o4t3n1k9u2s7dsnzv9.htm
The ALL keyword on the table statement determines getting the statistics for all levels of a class variable combined.
SUM is reserved for VAR variables. To get a total N please see this:
proc tabulate data=sashelp.class; class sex age; table sex*(n pctn) , age ; table (sex all)*(n pctn) , age ; table sex*(n pctn) , age all ; table (sex all)*(n pctn) , age all ; run;
You weren't very clear as to which particular "sum" you might want so I include example without any "all" and then examples of each dimension.
Also note, if an observation is removed because of a missing value, it is totally removed. It will be removed for the calculations based on other variables, even when those other variables do not have missing values.
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