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
[email protected]
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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