One solution is to format the data as if the follow-up period ended on day
2190.  For example,

TTT <- Survival_days
DDD <- Outcome

DDD[ TTT>2190 ] <- 0
TTT[ TTT>2190 ] <- 2190

survfit(Surv(TTT, DDD) ~ Gender)

-tgs



On Wed, Nov 20, 2013 at 3:01 PM, Dr.Vinay Pitchika
<dr.vinay.muc@gmail.com>wrote:

> Hello R users
>
> I have a question with Kaplan-Meier Curve with respect to my research. We
> have done a retrospective study on fillings in the tooth and their survival
> in relation to the many influencing factors. We had a long follow-up time
> (upto 8yrs for some variables). However, we decided to stop the analysis at
> the 6year follow up time, so that we can have uniform follow-up time for
> all the variables.
>
> I did play a bit with the formula and tried to stop the Kaplan-Meier curve
> at my desired time (2190days)or roughly 6 years. However, my question is I
> want to find the significance (log rank test) at this time point between
> the two curves; because I am not able to find a way to stop the survfit at
> this time point with my knowledge. Below is the code I used.
>
> Gender2<-survfit(Surv(Survival_days, Outcome)~Gender)
>
> plot (Gender2, xmax=2190, mark.time=FALSE, col=c(1:2), xlab="Survival time
> (Days)", ylab="Survival probability", main="Gender") # mark.time=FALSE will
> remove the censoring lines in the graph. If censoring lines are needed,
> then remove the mark.time section in the formula.
>
> legend("topright",c("Females", "Males"), col=(1:2), lwd=0.5)
>
> Am sure, the code in the first line has to be modified. Please help me with
> this and I will be very thankful to you.
>
> Thanks in advance
>
> Regards
> Vinay
>
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>
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