

Thanks for taking time to answer this






________________________________
De: Terry Therneau <therneau@mayo.edu>
Para: David Winsemius <dwinsemius@comcast.net>

.org>
Enviado: miércoles 28 de septiembre de 2011 23:03
Asunto: Re: [R] survival analysis: interval censored data

David,
  Thanks for taking time to answer this.

   When there are interval censored data, computation of the MLE first
finds a minimal number of time points such that every interval with a 
death contains one of those points.  Those are the points at which the
final curve can have a jump.  The algorithm, more or less is:
    1. On the real line, put a pair of parenthesis down for each
interval with an event "(" at the left end ")" at the right.
    2. Now scan down the line for () pairs (pairs with nothing else in
the enclosed interval): the center of each will be one of the jump
points.

  With slight variations for left truncation.

Terry T.

On Wed, 2011-09-28 at 16:33 -0400, David Winsemius wrote:
> On Sep 28, 2011, at 10:56 AM, Ruth Arias wrote:
> 
> >
> >
> > hallo terry:
> >
> > I attached araceae data set,
> 
> The usual survival analysis via the Kaplan-Meier method only make  
> estimates at the time of events. When you tabulate your data, you see 
> that there were no events for the missing (starting) "time" rows in  
> those categories during the intervals that you are questioning as  
> missing:
> 
> xtabs( ~ time+time2+categoria+event, data=araceae)
> , , categoria = C, event = 0
> 
>        time2
> time   2005 2006 2007 2008 2009 2010
>    2004    0   23    1    3    1   22
>    2005    0    0    0    0    0    0
>    2007    0    0    0    0    4   19
>    2008    0    0    0    0    0    0
>    2009    0    0    0    0    0    0
> 
> , , categoria = E, event = 0
> 
>        time2
> time   2005 2006 2007 2008 2009 2010
>    2004    0   22    0    7    3   21
>    2005    0    0    1    1    0    0
>    2007    0    0    0    0    0   29
>    2008    0    0    0    0    0    0
>    2009    0    0    0    0    0    1
> 
> 
> , , categoria = C, event = 1
> 
>        time2
> time   2005 2006 2007 2008 2009 2010
>    2004    0    5    2    3    0    3
>    2005    0    0    0    0    0    0
>    2007    0    0    0    2    3    2
>    2008    0    0    0    0    1    0
>    2009    0    0    0    0    0    0
> 
> , , categoria = E, event = 1
> 
>        time2
> time   2005 2006 2007 2008 2009 2010
>    2004    7    2    1    1    3    4
>    2005    0    0    0    1    0    0
>    2007    0    0    0    3    1    3
>    2008    0    0    0    0    0    0
>    2009    0    0    0    0    0    0
> 
> >
> > when I use this:
> >
> > surara<-survfit(Surv(time,time2,event)~categoria)
> >
> > Call: survfit(formula = Surv(time, time2, event) ~ categoria)
> >
> >             records n.max n.start events median 0.95LCL 0.95UCL
> > categoria=C      94    63       0     21     NA      NA      NA
> > categoria=E     111    77       0     26     NA      NA      NA
> >> summary(surara)
> > Call: survfit(formula = Surv(time, time2, event) ~ categoria)
> >
> >                 categoria=C
> >  time n.risk n.event entered censored survival std.err lower 95% CI  
> > upper 95% CI
> >  2006     63       5       0       23    0.921  0.0341        
> > 0.856        0.990
> >  2007     35       2      30        1    0.868  0.0483        
> > 0.778        0.968
> >  2008     62       5       1        3    0.798  0.0536        
> > 0.700        0.910
> >  2009     55       4       0        5    0.740  0.0570        
> > 0.636        0.861
> >  2010     46       5       0       41    0.660  0.0611        
> > 0.550        0.791
> >
> >                 categoria=E
> >  time n.risk n.event entered censored survival std.err lower 95% CI  
> > upper 95% CI
> >  2005     71       7       3        0    0.901  0.0354        
> > 0.835        0.973
> >  2006     67       2       0       22    0.875  0.0391        
> > 0.801        0.955
> >  2007     43       1      36        1    0.854  0.0432        
> > 0.774        0.943
> >  2008     77       5       0        8    0.799  0.0469        
> > 0.712        0.896
> >  2009     64       4       1        3    0.749  0.0502        
> > 0.657        0.854
> >  2010     58       7       0       51    0.658  0.0545        
> > 0.560        0.774
> 
> You see that your first survfit object is offering a simple sum of  
> 'time2' columns of that tabulation as its 'n.event' values. It's  
> 'n.risk' tabulation is not taking note of whether a case started in  
> any particular prior interval. The n.risk sum appears to be the sum of 
> persons surviving from the prior year less any decedents plus any  
> entrants as reflected in "future" events on that row    You notice 
> that there are missing years even in that report: 2004,2005 for  
> category C and 2004 for category E since there are no events in  
> columns for those 'time2' values.
> 
> >
> > but whe I included type=interval,
> >
> >> suraraint<- 
> >> survfit(Surv(time,time2,event,type='interval')~categoria) # falta 
[[elided Yahoo spam]]
> >> summary(suraraint)
> > Call: survfit(formula = Surv(time, time2, event, type = "interval") ~
> >     categoria)
> >
> >                 categoria=C
> >  time n.risk n.event survival std.err lower 95% CI upper 95% CI
> >  2004  95.00   13.14    0.862  0.0354        0.795        0.934
> >  2007  31.86    7.19    0.667  0.0695        0.544        0.818
> >  2008   1.67    1.67    0.000     NaN           NA           NA
> >
> >                 categoria=E
> >  time n.risk n.event survival std.err lower 95% CI upper 95% CI
> >  2004  112.0   18.47    0.835  0.0351        0.769        0.907
> >  2005   40.5    1.06    0.813  0.0401        0.738        0.896
> >  2007   37.5    7.46    0.651  0.0620        0.540        0.785
> 
> 
> The second object's n.event, when Surv() was constructed with  
> type="interval", has values based on the starting 'time' rows,  but I  
> am unable to deduce the estimating algorithm. I remember Therneau  
> saying it wasn't a simple algorithm. The 2008 row in category C has  
> one  entry of 1 in the next year and there were no censoring for C- 
> entrants in that year. Why the n.event is 1.67 I cannot say, but at  
> least the n.event does not exceed the n.risk.  The code or a copy of 
> Therneau and Grambsch would be sensible places to look for answer by  
> my initial efforts in those direction have not illuminated me.
> 
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