I think there is a much simpler explanation.
The survey design object has eight observations, two per country. With a sample size of two per
country it is hardly surprising that country-specific estimates are not very precise. The actual data has
hundreds of thousands of observations per country, so it will have more precise estimates.
Grouping the data doesn't make a difference for model-based glm estimation, where it is simply a
computational convenience. It *does* make a difference for design-based estimation, because it
changes the design.
-thomas
On Tue, 13 Oct 2009, Laust wrote:
Dear David,
Thanks again for your input! I realize that I did a bad job of
explaining this in my first email, but the setup is that in Finland
persons who die are sampled with a different probability (1) from
those who live (.5). This was done by the Finnish data protection
authorities to protect individuals against identification. In the rest
of the countries everyone is sampled with a probability of 1. The data
that I am supplying to R is summarized data for each country
stratified by case status. Another way of organizing the data would
be:
# creating data
listc <- c("Denmark","Finland","Norway","Sweden")
listw <- c(1,2,1,1)
listd <- c(1000,1000,1000,2000)
listt <- c(755000,505000,905000,1910000)
list.cwdt <- c(listc, listw, listd, listt)
country2 <- data.frame(country=listc,weight=listw,deaths=listd,time=listt)
I hope that it is clearer now that for no value of the independent
variable 'country' is the rate going to be zero. I think this was also
not the case in my original example, but this was obscured by my poor
communication- & R-skills. But if data is organized this way then
sampling weight of 2 for Finland should only be applied to the
time-variable that contains person years at risk and *not* to the
number of deaths, which would complicate matters further. I would know
how to get this to work in R or in any other statistical package.
Perhaps it is - as Peter Dalgaard suggested - the estimation of the
dispersion parameter by the survey package that is causing trouble,
not the data example eo ipso. Or perhaps I am just using survey in a
wrong way.
Best
Laust
****
Post doc. Laust Mortensen, PhD
Epidemiology Unit
University of Southern Denmark
On Mon, Oct 12, 2009 at 3:32 PM, David Winsemius <dwinsem...@comcast.net> wrote:
I think you are missing the point. You have 4 zero death counts associated
with much higher person years of exposure followed by 4 death counts in the
thousands associated with lower degrees of exposures. It seems unlikely that
these are real data as there are not cohorts that would exhibit such lower
death-rates. So it appears that in setting up your test case, you have
created an impossibly unrealistic test problem.
--
David
On Oct 12, 2009, at 9:12 AM, Laust wrote:
Dear Peter,
Thanks for the input. The zero rates in some strata occurs because
sampling depended on case status: In Finland only 50% of the non-cases
were sampled, while all others were sampled with 100% probability.
Best
Laust
On Sat, Oct 10, 2009 at 11:02 AM, Peter Dalgaard
<p.dalga...@biostat.ku.dk> wrote:
Sorry, forgot to "reply all"...
Laust wrote:
Dear list,
I am trying to set up a propensity-weighted regression using the
survey package. Most of my population is sampled with a sampling
probability of one (that is, I have the full population). However, for
a subset of the data I have only a 50% sample of the full population.
In previous work on the data, I analyzed these data using SAS and
STATA. In those packages I used a propensity weight of 1/[sampling
probability] in various generalized linear regression-procedures, but
I am having trouble setting this up. I bet the solution is simple, but
I’m a R newbie. Code to illustrate my problem below.
Hi Laust,
You probably need the package author to explain fully, but as far as I
can see, the crux is that a dispersion parameter is being used, based on
Pearson residuals, even in the Poisson case (i.e. you effectively get
the same result as with quasipoisson()).
I don't know what the rationale is for this, but it is clear that with
your data, an estimated dispersion parameter is going to be large. E.g.
the data has both 0 cases in 750000 person-years and 1000 cases in 5000
person-years for Denmark, and in your model they are supposed to have
the same Poisson rate.
summary.svyglm starts off with
est.disp <- TRUE
and AFAICS there is no way it can get set to FALSE. Knowing Thomas,
there is probably a perfectly good reason not to just set the dispersion
to 1, but I don't get it either...
Thanks
Laust
# loading survey
library(survey)
# creating data
listc <-
c("Denmark","Finland","Norway","Sweden","Denmark","Finland","Norway","Sweden")
listw <- c(1,2,1,1,1,1,1,1)
listd <- c(0,0,0,0,1000,1000,1000,2000)
listt <- c(750000,500000,900000,1900000,5000,5000,5000,10000)
list.cwdt <- c(listc, listw, listd, listt)
country <-
data.frame(country=listc,weight=listw,deaths=listd,yrs_at_risk=listt)
# running a frequency weighted regression to get the correct point
estimates for comparison
glm <- glm(deaths ~ country + offset(log(yrs_at_risk)),
weights=weight, data=country, family=poisson())
summary(glm)
regTermTest(glm, ~ country)
# running survey weighted regression
svy <- svydesign(~0,,data=country, weight=~weight)
svyglm <- svyglm(deaths ~ country + offset(log(yrs_at_risk)),
design=svy, data=country, family=poisson())
summary(svyglm)
# point estimates are correct, but standard error is way too large
regTermTest(svyglm, ~ country)
# test indicates no country differences
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--
O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalga...@biostat.ku.dk) FAX: (+45) 35327907
______________________________________________
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT
______________________________________________
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Thomas Lumley Assoc. Professor, Biostatistics
tlum...@u.washington.edu University of Washington, Seattle
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