LOCF with visit missing in the data set

[fengyuwuzu]

I want to use LOCF to impute missing values, but the missing visits are not present in the dataset. 

In this case, how do I check the missing visits and set the value to missing value first, and then use retain to do LOCF? 

data visit;
input ID $ avisitn result;
datalines;
A 0 4.1
A 1 3.9
A 2 4.3
A 4 5.0
A 8 4.5
A 12 5.1
A 16 5.3
A 18 4.9
A 20 4.7
B 0 4.2
B 1 3.7
B 2 4.4
B 4 5.2
B 12 4.5
B 16 5.2
B 18 4.8
B 20 4.9
C 0 4.6
C 1 4.9
C 2 5.3
C 8 5.5
C 16 5.6
C 18 5.9
C 20 5.7
;
run;

/* avisitn is 0, 1, 2, 4, 8, 12, 16, 18, 20* /
/* A has all visits, B misses avisitn 8, C misses avisit 4 & 12 */

[maguiremq]

I think this takes care of it. The `PROC FREQ` with the `SPARSE` option expands the table to include the missing `avisitn` records.

The rest just is some PDV manipulation.

proc freq
	data = visit noprint;
		tables id * avisitn / out = visit_1 (drop = percent count) sparse;
run;

data visit_2;
	merge
			visit
			visit_1;
	by		id avisitn;
	retain 	result2;
		
		if	first.id then result2 = .;
		if 	result ~= . then result2 = result;
			
run;

ID avisitn result result2 
A 0 4.1 4.1 
A 1 3.9 3.9 
A 2 4.3 4.3 
A 4 5.0 5.0 
A 8 4.5 4.5 
A 12 5.1 5.1 
A 16 5.3 5.3 
A 18 4.9 4.9 
A 20 4.7 4.7 
B 0 4.2 4.2 
B 1 3.7 3.7 
B 2 4.4 4.4 
B 4 5.2 5.2 
B 8 . 5.2 
B 12 4.5 4.5 
B 16 5.2 5.2 
B 18 4.8 4.8 
B 20 4.9 4.9 
C 0 4.6 4.6 
C 1 4.9 4.9 
C 2 5.3 5.3 
C 4 . 5.3 
C 8 5.5 5.5 
C 12 . 5.5 
C 16 5.6 5.6 
C 18 5.9 5.9 
C 20 5.7 5.7 

[Tom]

Build up a dataset with every visit for every subject.

proc sql noprint;
 create table skeleton as
 select id,avisitn
 from (select distinct id from visit)
    , (select distinct avisitn from visit)
 order by id,avisitn
 ;
quit;

data full ;
  merge skeleton visit (in=in1);
  by id avisitn ;
  realdata=in1;
run;

Then you can use LOCF technique to replace the missing values.

data locf ;
  update full(obs=0) full;
  by id;
  output;
run;

Results:

Obs    ID    avisitn    result    realdata

  1    A         0        4.1         1
  2    A         1        3.9         1
  3    A         2        4.3         1
  4    A         4        5.0         1
  5    A         8        4.5         1
  6    A        12        5.1         1
  7    A        16        5.3         1
  8    A        18        4.9         1
  9    A        20        4.7         1
 10    B         0        4.2         1
 11    B         1        3.7         1
 12    B         2        4.4         1
 13    B         4        5.2         1
 14    B         8        5.2         0
 15    B        12        4.5         1
 16    B        16        5.2         1
 17    B        18        4.8         1
 18    B        20        4.9         1
 19    C         0        4.6         1
 20    C         1        4.9         1
 21    C         2        5.3         1
 22    C         4        5.3         0
 23    C         8        5.5         1
 24    C        12        5.5         0
 25    C        16        5.6         1
 26    C        18        5.9         1
 27    C        20        5.7         1

[fengyuwuzu]

Thank you very much, Maguiremg and Tom!

I learned from both of you. very nice ideas.