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SV: st: Analysis of event history data


From   "Kristian Thor Jakobsen" <KRJ@dm.dk>
To   <statalist@hsphsun2.harvard.edu>
Subject   SV: st: Analysis of event history data
Date   Mon, 19 Mar 2012 13:30:21 +0100

Thanks, Nick. -reshape- is a big help. But what if I have time-varying variables that I would like to carry over as well, but not with same intervals. For example:

Id	y_1001	y_1002	y_1003 ...	y_1101	area_10 	area_11
1	1	1	0	0	10	5

If I do -reshape using y_ as the identifier I would get something like:

Id	j	y_	area_10	area_11
1	1001	1	10	5
1	1002	1	10	5
1	1003	0	10	5
.
.
.1	1101	0	10	5

But I would like to have something like:

Id	j	y_	area	
1	1001	1	10	
1	1002	1	10	
1	1003	0	10	
.
.
.
1	1101	0	5

Is that possible with -reshape-? Or would I have to convert the yearly time-varying variables into weekly first?

Thanks again,
Kristian	

-----Oprindelig meddelelse-----
Fra: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] På vegne af Nick Cox
Sendt: 19. marts 2012 12:43
Til: statalist@hsphsun2.harvard.edu
Emne: Re: st: Analysis of event history data

For most Stata purposes your data would indeed be better reshaped to a long data structure or shape or form (some people do say "format", but in a Stata context format implies -format-, etc.).

reshape long y_ , i(id) j(time)
rename y_ status

should do it. See also -tsspell- (SSC) and

SJ-7-2  dm0029  . . . . . . . . . . . . . . Speaking Stata: Identifying spells
        . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  N. J. Cox
        Q2/07   SJ 7(2):249--265                                 (no commands)
        shows how to handle spells with complete control over
        spell specification

as well as the literature on survival analysis with which you are evidently familiar.

Nick

On Mon, Mar 19, 2012 at 11:32 AM, Kristian Thor Jakobsen <KRJ@dm.dk> wrote:

> I am trying to do an analysis of transition in and out of public 
> income transfers. My data is organized roughly the following way:
>
> Id      y_1001  y_1002  y_1003
> 1       0       1       0
> 2       0       0       0
> 3       1       1       0
>
> This means that I have the weekly status of each individual from 1991 
> to 2011. But in order to any sort of analysis I would guess that I had 
> to convert the data into the following way instead (for example 
> survival
> analysis):
>
> Id      Status  Time
> 1       0       1
> 1       1       2
> 1       0       3
> 2       0       1
> 2       0       2
> 2       0       3
> 3       1       1
> 3       1       2
> 3       0       3
>
> Is that correct, and if so, does there exist a smart way to convert 
> the data from one format into the other? Or can I perhaps use the data 
> as given?
>

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