Dear all,
I send you this message to thank all of you for the responses you gave
to my Allstat query, and to share your contributions within a group of
interested people. Please find attach my original query and your
responses. The study I am working on is called GEIRD (Genes Environment
Interactions in Respiratory Diseases, www.geird.org), which also
includes subjects who took part in the ECRHS.

Best wishes to all of you
Alessandro Marcon


-- 

Alessandro Marcon, PhD
Unit of Epidemiology&    Medical Statistics
Department of Public Health and Community Medicine
University of Verona
Strada Le Grazie 8, 37134 Verona, Italy
tel. +39 045 8027668 fax +39 045 8027154




*QUERY: Re-using data from case-control studies*

From: [log in to unmask] <mailto:[log in to unmask]>

Dear all

I wish to perform a "secondary" analysis on data collected in a
multicase-control design (where the primary aim was to investigate the
association between genetic determinants and respiratory diseases).
My aim is to study the association between the case-control status (main
independent covariate) and a continuous measure of exercise capacity
(dependent covariate), while adjusting for several potential confounders
(gender, age, smoking status, etc). I am currently using a standard
linear multiple regression model with exercise capacity = Y and the
case-control status and the other potential confounders as Xi.

Do you think that the above statistical analysis is correct, and do you
have any reference to support that?

I have found some reference on re-using data from case-control studies
by logistic regression (1,2), but no reference to the use of linear
regression models.


References:
1) Lee AJ, McMurchy L, Scott AJ. Re-using data from case-control
studies. Stat Med. 1997 Jun 30;16(12):1377-89.
2) Nagelkerke NJ, Moses S, Plummer FA, Brunham RC, Fish D. Logistic
regression in case-control studies: the effect of using independent as
dependent variables. Stat Med. 1995 Apr 30;14(8):769-75.


Best wishes to all!
Alessandro Marcon

*
*

*RESPONSES*

You can use re-weight the data to population based sample. It can be 
implemented using survey weights in stata or using the weights argument 
in glm() in R.

However calculating the weight in a realistic manner can be tricky.

Are you by any chance talking about the GABRIEL study? Which cohort are 
you working with? I am working with the ECRHS.

Regards, Adai

Hi Allessandro,

I see nothing wrong with performing such secondary analysis, but there

is no need to include case-control status as a dummy variable in your

dependent variables.

I've no references on this, but have worked for a number of years in

statistical genetics and its common in this area, just as within other

epidemiological studies to have a primary outcome and then perform

secondary analyses that are more detailed or look at alternative

end-points.

What you do have to consider is the issue of

multiple-testing/data-dredging.I.e. be aware that setting p < 0.05

is arbitrary in the first place and if you do twenty tests with this

threshold, then by chance alone one will give a "significant" result

by chance alone.This would be further excaberated if you were to

test multiple endpoints.Similarly there are the same issues when

testing multiple genetic determinants (which I interpret to be genetic

markers such as SNPs, but you don't state what these are, if you are

using SNPs then you should be using the Armitage-Trend Test as

advocated by Sasieni 1997 http://www.ncbi.nlm.nih.gov/pubmed/9423247).

If the SNPs are syntenic then you might consider haplotype analyses

too.

I wrote a website a long time ago which is no longer online that some

people have found useful to get them started in this area.It used to

be at http://slack.ser.man.ac.uk/ but is archived at

http://www.archive.org/ (although at the time of writing their servers

are suffering technical problems).

There is a huge wealth of work on genetic epidemiology (its the area I

did my MSc in and that only scratched the surface!) and hopefully some

of the references on my old site will get you started (when the

archive is back up and running, if its not let me know I have a copy

of the site I could probably mail to you).

Good luck,

Neil

Alessandro,

That's OK. However, if you have any categorical covariates that have

more than two categories, then you could consider modelling these as

factors, within an ANCOVA framework. You might also wish to consider

possible interactions between the independent variables.

Allan

Dear Alessandro,

An interesting question. A few thoughts.

Case-control studies are a bit odd. They are almost always

done with some level of matching - this may be explicit, in

a formal matched study, and/or implicit, in the eligibility

criteria for cases and controls. In essence you construct

two biased samples, with very different sampling weights

(close to 100% for cases, and usually much much smaller for

controls), with (possibly) matching-induced confounding

between the matching variables and the case-control status

(at least). Analysis of CC studies makes allowance for all

of this.

So, what can you say, about what populations, from such a

sample?

I can see lots of questions which are answerable, but lots

more which are not. I don't see why your question is not

answerable, but it would be essential to know all about how

the cases and controls were collected. At a minimum you will

need to include all the matching variables, explicit and

implicit in every analysis, whether they are significant or

not. I don't personally know of any analyses similar to

yours, but the approach you suggest is sound.

However, to make inferences about any population beyond the

study participants, the issue of weighting raises its ugly

head. Each control represents typically thousands of people,

while each case typically represents one in the underlying

population. You need to show that this does not affect your

conclusions, which might not be possible.

I think you can do something interesting, but you will need

to think hard, and do a lot work to show that it means anything.

Best of luck!

Anthony Staines


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