I am now very confused by your intuitive argument.
This was a population-based Case�Control Study
comparing a specific enzyme in the serum of infected
patients (cases) to that in healthy non-infected
controls. We compared these levels using a t-test
after log-tranforming the data. Is this
incorrect?There are many similar studies in the
literature. Am I to understand that they are all
incorrectly analyzed?
Am sorry but I do not get it.
Ricardo.
--- Philip Ryan <[email protected]> wrote:
> Ricardo
>
> Leaving aside the question of relative numbers of
> cases and controls, I
> wonder if the reviewers remarked on your choice of
> analysis. That is to
> say, in a case control study the outcome is the case
> control status, not
> the antecedent exposure (in your study the biomarker
> level). A t-test
> reverses this sense of the study design, as the
> exposure is now the outcome
> and the case control status is (I was taught) forced
> unnaturally to be the
> "predictor". In modelling terms, keep the outcome
> defined by the study
> design on the left hand side. I would choose a
> logistic model, either
> keeping the biomarker level continuous if you
> believe there is a linear
> dose response with the log odds or perhaps with
> dummies of ordered
> categories of the biomarker if you wish to explore
> the functional nature of
> the relationship.
>
> Phil
>
>
>
>
> At 05:32 AM 23/03/2004 -0800, you wrote:
> >Thank you Michel,
> >
> >I would like to clarify two points:
> >
> >1. We had more cases than controls because of
> >budgetary constrains. It was easier and less
> expensive
> >to enroll cases than controls.
> >
> >2. The main outcome of interest was a serum
> biomarker
> >measured on a continuous scale and log transformed
> for
> >the analysis. A t-test was used to compare cases
> and
> >controls and therefore no OR computed.
> >
> >Best,
> >Ricardo.
> >
> >
> >
> >--- Michel Camus <[email protected]> wrote:
> > > Ricardo Ovaldia wrote:
> > >
> > > >(...) We recently submitted a manuscript for
> > > publication to
> > > >a major medical journal. It was a case-control
> > > study
> > > >with 329 cases and 126 controls. One of the
> > > reviewers
> > > >wrote that "to have such a larger number of
> cases
> > > was
> > > >statistically atypical" and asked if the
> "authors
> > > find
> > > >that the use of the same control for multiple
> > > patients
> > > >significantly limits results"?
> > > >
> > > >I never heard of any biases or other problems
> cause
> > > by
> > > >having more cases than controls in a study. We
> had
> > > >sufficient power and the difference for our
> main
> > > >outcome was highly significant (less than
> 0.00001).
> > > Am
> > > >I missing something or is it that this reviewer
> > > does
> > > >not understand the case-control designed? By
> the
> > > way
> > > >this was not a matched study design.
> > > >Thank you,
> > > >Ricardo.
> > > >
> > > >
> > > Dear Ricardo,
> > > There is no problem per se with having less
> controls
> > > than cases, though
> > > it should raise some eyebrows.
> > > The critique of using "the same control for
> multiple
> > > patients" suggests
> > > the reviewer's misunderstanding of an unmatched
> > > design.
> > > A smaller number of controls for a single group
> of
> > > cases is "atypical"
> > > still.
> > > One usually chooses an equal or larger group of
> > > controls to increase
> > > power to be able to detect even a small odds
> ratio
> > > when exposure is
> > > relatively rare.
> > > A smaller number of controls than cases suggests
> > > that the investigators
> > > had more cases than needed given an expected a
> > > priori a large relative
> > > risk (>5) and a high prevalence of exposure
> (>75%)
> > > among controls (cf.
> > > Schlesselmann, 1982, p.155). Could it not then
> be
> > > construed that the
> > > investigators knew enough beforehand not to do a
> > > study?...
> > > With respect to the outcome measure, I do not
> > > understand how you can say
> > > from a case-control study that "the difference
> for
> > > our main outcome was
> > > highly significant (less than 0.00001)".
> Usually
> > > the measure of effect
> > > in a case-control study is an odds ratio, not a
> > > difference (in rates?).
> > >
> > > Michel
> > >
> > > ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
> ~ ~
> > > ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
> > > ~ ~ ~ ~ ~
> > >
> > > Michel Camus, Ph.D.
> > >
> > > �pid�miologue, Div. Biostatistique et
> �pid�miologie,
> > > DGSESC, Sant� Canada
> > >
> > > Epidemiologist, Biostatistics and Epidemiology
> Div.,
> > > HECSB, Health Canada
> > >
> > > Courriel / e-mail : [email protected]
> > > <mailto:[email protected]>
> > >
> > > T�l�phone / phone : (514) 850-0157
> > >
> > > T�l�copieur / fax : (514) 850-0836
> > >
> > > ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
> ~ ~
> > > ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
> > > ~ ~ ~ ~ ~
> > > ==============================
> > >
> > >
> > >
> > >
> > > *
> > > * For searches and help try:
> > > *
> > >
> http://www.stata.com/support/faqs/res/findit.html
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> >
> >
> >__________________________________
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>
> Philip Ryan
> Associate Professor,
> Department of Public Health
> Associate Dean (Information Technology)
> Faculty of Health Sciences
> University of Adelaide 5005
> South Australia
> tel 61 8 8303 3570
> fax 61 8 8223 4075
> http://www.public-health.adelaide.edu.au/
> CRICOS Provider Number 00123M
>
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