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RE: st: RE: Goodness of fit using Cox-snell residuals


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: Goodness of fit using Cox-snell residuals
Date   Thu, 1 Jun 2006 16:13:48 +0100

Nick 
n.j.cox@durham.ac.uk 

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Nick Cox
> Sent: 01 June 2006 16:04
> To: statalist@hsphsun2.harvard.edu
> Subject: RE: st: RE: Goodness of fit using Cox-snell residuals
> 
> 
> It just makes no sense to feed the Cox-Snell
> residuals to -stset-. You already set up 
> the survival problem using -date_visit-. 
> 
> Once you have the Cox-Snell residuals, there 
> are various things you can usefully do with them, but 
> feeding them to -stset- is not one of those
> things. 
> 
> As -stset-is telling you, many of the residuals 
> are negative, so the operation makes no sense on that ground 
> alone.. 
> 
> Nick 
> n.j.cox@durham.ac.uk 
> 
> Emelda Okiro
>  
> > Calrification
> > Am using stata 8
> > This is what my data looks like
> > 
> > Id    sex  date_visit        age        failure    
> > 1     0     04jun2004         28          0
> >   1     0    12jun2004         28          0 
> >    1     0   18jun2004         28          0 
> >    1     0   16jul2004         29          0
> >   1     0   13aug2004         30          0 
> >    2     0   01mar2002         0          0 
> >    2     0   27mar2002         1          0 
> >   2     0   15apr2002          2          0 
> >   2     0   18apr2002          2          1 
> >   2     0   29apr2002          2          0 
> > 
> > basic time scale is calender time declared on the stset 
> > origin and scale control the mapping from the basic time 
> > scale onto the
> > time scale on which the analysis is to be performed
> > . 
> > . stset date_visit, id (rsv) failure(lrti) enter(time
> > date_origin)origin(time d(31jan2002)) exit(time date_exit) scale(1)
> > 
> >                 id:  rsv
> >      failure event:  lrti != 0 & lrti < .
> > obs. time interval:  (date_visit[_n-1], date_visit]
> >  enter on or after:  time date_origin
> >  exit on or before:  time date_exit
> >     t for analysis:  (time-origin)
> >             origin:  time d(31jan2002)
> > 
> > --------------------------------------------------------------
> > ----------------
> >     29979  total obs.
> >         0  exclusions
> > --------------------------------------------------------------
> > ----------------
> >     29979  obs. remaining, representing
> >       469  subjects
> >       952  failures in multiple failure-per-subject data
> >    377180  total analysis time at risk, at risk from t =         0
> >                              earliest observed entry t =         0
> >                                   last observed exit t =      1177
> > 
> >  
> > 
> > . **** Checking the goodness of fit of the final model
> > . * evaluated by using Cox-Snell residuals
> > . * if the model fits the data well then the true cumulative hazard
> > function conditional on the covariate vector should have an 
> > exponential
> > distribution with a hazard rate of one
> > . quietly xi: stcox i.currentagegrp sex i.siblings_un6 i.main_fuel
> > i.hse_toilet i.babies_bor i.education i.family_children
> > i.interaction_un6 i.siblingssch_un6 i.siblingsroom_ov6 i.female_sibs
> > poor  i.weaning i.job_desc, nohr mgale(mg)
> > 
> > . * compute cox-snell residuals
> > . predict cs, csnell
> > (663 missing values generated)
> > 
> > . *re stset using cs residuals as the time variable (look at 
> > the output)
> > the missing values are truly missing but it is omitting some of the
> > observations ????? It is also assuming single failure single record
> > which is incorrect as shown above my data set has multiple records
> > multiple failure-per-subject data.
> > 
> > . stset cs, failure(lrti)
> > 
> >      failure event:  lrti != 0 & lrti < .
> > obs. time interval:  (0, cs]
> >  exit on or before:  failure
> > 
> > --------------------------------------------------------------
> > ----------------
> >     29979  total obs.
> >       663  event time missing (cs>=.)                         
> >   PROBABLE
> > ERROR
> >      1046  obs. end on or before enter()
> > --------------------------------------------------------------
> > ----------------
> >     28270  obs. remaining, representing
> >       925  failures in single record/single failure data
> >       925  total analysis time at risk, at risk from t =         0
> >                              earliest observed entry t =         0
> >                                   last observed exit t =  .8936376
> > 
> > Does anyone know how cs residuals are computed in this kind 
> > of data and
> > how I can specify multiple failure multiple recors when using cs
> > residuals as the time variable
> 
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> 

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