Displaying 2 results from an estimated 2 matches for "fitf".
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2003 Aug 04
1
coxph and frailty
...amp; id<150] <- 3
group[id>=150 & id<200] <- 4
group[id>=200 & id<250] <- 5
group[id>=250 & id<300] <- 6
group[id>=300] <- 7
I estimate the following model, using the Pbc data (with time-varying
covariates) from Therneau and Grambsch's book:
fitf <- coxph(Surv(start,stop,event==2) ~ age + log(bili) + log(protime) +
log(albumin) + edema + frailty(group),
na.action=na.exclude, data=Pbcseq)
Then I obtain:
> fitf[10]
$frail
[1] 0.06273372 0.16192093 0.10050877 0.37716999 -0.20853156...
2009 Jun 23
0
Fractional Polynomials in Competing Risks setting
...transformation, if any, is best. This was possible as
the Cox model was the underlying model. However, I am now at the
situation where the assumption that the competing risks are
independent is no longer true and therefore I cannot use the Cox
model.
The code I used to get the MFP model was:
coxfitf <- mfp(Surv(with.Withtime,with.Wcens)~fp(all.age),family=cox,data=nearma,select=0.05,verbose=TRUE)
where with.Withtime is the time to treatment withdrawal, with.Wcens is
the censoring indictor for the event and all.firstint is the age at
baseline.
To look at the competing risks regression model...