st: AW: Data management question for survival analysis problem

["Martin Weiss" <martin.weiss1@gmx.de>]

<> 

Sounds like a case for the -sum()- function

*************
clear*

inp ID  VI     AGE    CONTVAR    BINVAR  
1   1      60      .         .        
1   2      62      .         .       
1   3      66      12        0       
1   4      67      14        0       
1   5      72      17        1       

2   1      60  20        1       
2   2      62      .         .       
2   3      66      12        0       
           
3   1     60       22        1          
3   2     62       12        0         
3   3     66       16        0       
3   4     67       18        1       
3   5     72       19        1        

4   1     60        22        1       
4   2     62        24        1         
4   3     66        25        1      
end 
       
compress       
l, sepby(ID)   
      
bys ID: gen TDVAR=sum(BINVAR)

l, sepby(ID)   
*************



HTH
Martin


-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von MAY BAYDOUN
Gesendet: Montag, 13. April 2009 21:59
An: statalist@hsphsun2.harvard.edu
Betreff: st: Data management question for survival analysis problem

Dear Statalisters,

I am trying to do the following so I can run survival analysis using a
person-period file. I have data with id and visits and each visit is related
to a particular which is the time variable. I have a variable that changes
over visits and has a cutpoint at 16. I changed this variable from
continuous to binary (<=16:0 and >16: 1). Now, I want to create a
time-dependent variable, in such a way that if someone is zero for all
visits, he/she is zero. If they become "1" at a visit, they are 1 in
subsequent visits unless they score another "1" in which case, they become a
"2". I don't want to have missing values for this new variable. What is the
easiest way to do this? Below is an example:

ID  VI     AGE    CONTVAR    BINVAR  TDVAR
1   1      60      .         .       .  
1   2      62      .         .       .
1   3      66      12        0       0
1   4      67      14        0       0 
1   5      72      17        1       1

2   1      etc.    20        1       1
2   2              .         .       1
2   3              12        0       1

3   1              22        1       1    
3   2              12        0       1   
3   3              16        0       1
3   4              18        1       2
3   5              19        1       3  

4   1              22        1       1 
4   2              24        1       2   
4   3              25        1       3

Thanks for any help you can provide. 

Sincerely yours,

May







May Baydoun, PhD in Epidemiology (UNC-Chapel Hill)  Staff Scientist,
National Institute on Aging, NIH/IRP,  Biomedical Research Center,
Baltimore, MD


      
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