Maria:
What you are looking for (propensity score matching on survey data) is
discussed in lab 5 components of this series using matchit and matching
package:
https://www.youtube.com/playlist?list=PL2yD6frXhFob_Mvfg21Y01t_yu1aC9NnP
Regards,
Ehsan
https://ehsank.com/
On Fri., Oct. 9, 2020, 4:0
Dear list:
I am getting p-values and confidence intervals contradictory in
svyglm() output from the survey package.
Here is a reproducible example:
https://ehsanx.github.io/SurveyDataAnalysis/#114_Regression_analysis
This problem can be easily fixed by setting appropriate df.resid in
the summary
Dear R-list,
I am wondering whether anyone could explain what'd be the difference between
running a 'generalized additive regression' versus 'generalized linear
regression' with splines.
Are they same models theoretically? My apologies if this is a silly question.
Any comments or direction to
Dear List,
Just wondering, is there a Bayesian version of weighted regression
available in the literature (to handle survey weights, say)? If yes,
could you suggest me a reference? Does MCMCregress handle weights?
cheers,
Ehsan
__
R-help@r-project.org
Dear List,
Here is an example of survival data in counting process format
(detailed record of each day)
> data[data$Id == 11,]
# extracted one person's record
Id Event Fup Start Stop sex Drug1
601 11 0 6 01 0 0
602 11 0 6 12 0 0
603 11 0 6 2
/references will be highly appreciated.
cheers,
Ehsan
On Tue, Feb 21, 2012 at 11:11, Ehsan Karim wrote:
>
>
>> Subject: Re: bootstrap in time dependent Cox model?
>> From: thern...@mayo.edu
>> To: wilds...@hotmail.com
>> CC: r-help@r-project.org
>> Date: Tue, 21 Feb 20
Dear R-list,
I am wondering how to perform a bootstrap in R for the weighted time
dependent Cox model (Andersen–Gill format, with multiple observations
from each patients) to obtain the bootstrap standard error of the
treatment effect.
Below is an example dataset. Would 'censboot' be appropriate
Sorry: there was an error in the weight calculation, fixed version is
the following, but still the final estimates differ as explained in
the original email:
#
require(survival)
require(eha)
data(heart)
head(heart)
follow <- heart$stop - heart$start
fit <- glm(transplan
Dear List,
After including cluster() option the coxreg (from eha package)
produces results slightly different than that of coxph (from survival)
in the following time-dependent treatment effect calculation (example
is used just to make the point). Will appreciate any explaination /
comment.
cheer
Dear List:
Wondering how to get around the following problem: any suggestions are welcome.
Cheers,
Ehsan
> install.packages("geepack")Warning in install.packages("geepack") : argument
> 'lib' is missing: using '/home/grad/student/Library/R/2.11/library'--- Please
> select a CRAN mirror for u
Dear List,
Must be a silly question, but I was wondering whether there is a direct way of
calculating "standard error of a HR or exp(coef)" from coxph objects
x <- coxph(Surv(time, status) ~ age + inst, lung)> xcoef exp(coef)
se(coef) zpage 0.0190 1.02 0.00925 2.06 0.04i
Dear List,
Can anyone please explain the difference between cluster() and
frailty() in a coxph? I am a bit puzzled about it. Would appreciate
any useful reference or direction.
cheers,
Ehsan
> marginal.model <- coxph(Surv(time, status) ~ rx + cluster(litter), rats)
> frailty.model <- coxph(S
Apology for reposting, but the format of earlier message got
distorted; hopefully this time it will be readable:
From: wilds...@hotmail.com
To: r-help@r-project.org
Subject: Longitudinal data with non-randomized subjects
Date: Sun, 1 May 2011 00:34:08 -0700
Dear List,
I have a theoretical quest
Dear List,
I have a theoretical question related to epidemiological data analysis:
If the treatment status (tx = 0,1) changes over time for the patients in a
non-randomized cohort, is there a way to estimate the treatment effect?
(i.e., after joining the study, some patients may have to wait fo
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