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

["Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov>]

not true - see p. 258 Example 2 of the Stata 9 survival manual.

Al F. 

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox
Sent: Thursday, June 01, 2006 10:04 AM
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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