On 7/19/19 10:19 AM, Denise b wrote:
Dear R users,
I am interested in estimating the effects of a treatment on two
time-to-event traits (on simulated data), accounting for the dependency
between the two time-to-event outcomes.
I precise that the events are NOT recurrent, NOT competitive, NOT ordered.
The individuals are NOT related and can have 0, 1 or the 2 events (in any
ordered).
So, I specified a time-to-event model with one Cox PH hazard function for
each outcome.
The two hazard functions are linked by a common subject-specific frailty
term (gamma distribution) to account for the dependency. The likelihood
function of that model would include two risks sets (1 for eachoutcome)
connected via the shared frailty term.
To fit that model, I used the coxph function (survival R package, T.
Thernaud):
coxph( Surv(Time, Status) ~ treatment*Event_type + strata(Event_type) +
frailty(ID), data=example)
If you have Event_type as a strata variable, it seems problematic that
it be also interacting as a fixed effect.
- where example is my dataset, with 2*N individuals (2 rows for each
individual, corresponding to each time-to-event outcome)
- Time = c(Time_outcome1, Time_outcome2)
How exactly do you expect the Surv-function to handle such a vector?
- Status=c(Event_outcome1, Event_outcome2)
The help page for Surv() says: "For multiple endpoint data the event
variable will be a factor, whose first level is treated as censoring."
So it, too, would not be "expecting" a two-item vector. Seems that the
multi-state formulation would make more sense.
- Event_type=c(rep(0,length(Time_outcome1)),rep(1,length(Time_outcome2))
I'm reasonably sure 0 would be interpreted as a censored observation.
- ID=c(ID,ID)
So, the model implies different baseline hazard function for each outcome
(argument strata) and estimate a treatment effect for each event type(
treatment*Event_type).
I doubt it. I'm pretty sure there would be a conflation of effects and
stratification.
Although the model returns some estimates close to what I expected
(simulation study), the problem is that most of the examples I found with
this type of formulation are for competitive time-to-event models (as
presented in Therneau's and Grambush's book "Modeling survival
data" (pages 240-260 and 175-185) and also in some info I found on
I cannot lay my hands on my copy of that text, but I'm fairly sure it
never mixed strata(.) and fixed effects for a variable in the same model.
I suppose this is where I should admit that I'm trained as an
epidemiologist and not as a statistician, but I've done a fair amount of
work with these models. Seems to me that your code are sufficiently out
of the mainstream practice that you would at least want to create a
simulated data-set and see if this results in agreement with assumptions.
--
David.
internet).
So, I am wondering if some of you have the same or different interpretation
of mime.
Otherwise do you have other functions to recommend me to fit the desired
model, such as coxme...? I also checked the package " mets" which also
describes several examples for competitive time-to-event traits, but not
for NON competitive events.
Thanks in advance for your help,
Denise
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