As part of a research paper, I would like to draw both weighted and
unweighted Kaplan-Meier estimates, the weight being the ’importance’ of the
each project to the mass of projects whose survival I’m trying to estimate.
I know that the function survfit in the package survival accepts weights and
p
Say, that I have two observations, one from time 0 to time 50, and a second
from time 0 to time 100, both of which are known to have failed, i.e. no
censoring. I would like to give double the weight to the second observation.
This is what I’ve tried to implement in the both pieces of code. Both pi
Thanks to all of you for your very helpful comments! As Terry suggested,
svykm is what I was looking for.
While testing that I get the same results with the package survey as with
the package survival, I encountered the issue of how to draw survival
curves. Apparently the implementations in the t
I’m trying to set up proportional hazard model that is stratified with
respect to covariate 1 and has an interaction between covariate 1 and
another variable, covariate 2. Both variables are categorical. In the
following, I try to illustrate the two problems that I’ve encountered, using
the lung da
I’m trying to set up proportional hazard model that is stratified with
respect to covariate 1 and has an interaction between covariate 1 and
another variable, covariate 2. Both variables are categorical. In the
following, I try to illustrate the two problems that I’ve encountered, using
the lung da
Hi Chris,
Yes, that is exactly the model I want to fit, i.e. a separate 'baseline'
hazard for each stratum (defined by cov1) and a coefficient for cov2 that is
different for each stratum.
Is suspect that there is a problem with the following line of your code.
sCox <- coxph(Surv(time, status)
I’m trying to set up Cox Proptional Hazard model with interactions between
time and the covariates (which are categorical). The problem that I face is
that how to define the interactions, i.e. “x+cutStart:x”, properly.
The code below illustrates the problem. R gives the error message ” X matrix
de
The output is as follows. My question is how to include only the interaction
terms where x is equal to "Maintained".
(x has two possible values "Maintained" and "Nonmaintained".)
--
View this message in context:
http://r.789695.n4.nabble.com/coxph-how-to-define-interaction-terms-tp4679162p46
Any ideas would be much appreciated; I suspect that this problem of
constructing the dummies applies not only to function coxph but to other
regression models in R as well. Effectively, my question is how to better
control for which dummies and interactions to include in the model and which
not.
T
In a rather simple regression, I’d like to ask the question, for high trees,
whether it makes a difference (for volume) whether a three is thick.
If my interpretation is correct, for low trees, i.e. for which trees$isHigh
== FALSE, the answer is yes.
The problem is how to "merge" the standard er
10 matches
Mail list logo
