Thank you to everyone that responded to my post.
Although I understand the advantage of using logistic
regression to analyze these data, I am trouble but the
fact that I can not compare the mean enzyme
concentrations between the infected and non-infective
patients while controlling for other covariates.
Naively I was assuming that I had a random sample of
two independent populations (infected and
not-infected) and that I could use a t-test to compare
the means and a linear regression to compare the means
while adjusting for other covariates and effect
modifiers such as age and race.
Thank you,
Ricardo.
--- "M. Moshaddeque Hossain" <[email protected]> wrote:
> Actually from logistic regression analysis, we get
> the ratio of the odds, to
> be more specific, as a proxy of the ratio of the
> risks associated with
> different exposure levels.
>
>
>
>
> -----Original Message-----
> From: Dario Consonni
> [mailto:[email protected]]
> Sent: Wednesday, March 24, 2004 1:57 PM
> To: [email protected]
> Subject: Re: Analysis of a case control study (was
> st: more cases than
> controls)
>
> I agree with Phil, in fact this is the reason why
> usually case-control
> studies are analyzed using logistic regression: you
> get OR - the
> relationship of risk of disease with increasing
> levels of exposure (the
> serum levels) - a measure much more informative than
> a simple t-test .
> Dario
>
>
> ----- Original Message -----
> From: "Philip Ryan" <[email protected]>
> To: <[email protected]>
> Sent: Wednesday, March 24, 2004 8:10 AM
> Subject: Analysis of a case control study (was st:
> more cases than controls)
>
>
> > Ricardo
> >
> > I do not mean to imply that the studies (of which
> I know nothing!) are
> > *incorrectly* analysed.
> >
> > Indeed, Hosmer and Lemeshow (Applied Logistic
> Regression, Wiley 1989)
> state
> > that, in the univariate case at least, the t test
> is equivalent to the
> > simple logistic model. They appeal to the
> analogous discriminant
> > function. This is somewhat qualified by their
> statements (p84 of first
> > edition):
> >
> > 1. "the most desirable univariate analysis
> involves fitting a univariate
> > logistic regression..."
> > 2. there are assumptions of normality when using
> the t test that are not
> > required in the logistic model (I note you appear
> to have taken care of
> this)
> >
> > and
> >
> > 3. ".. the t test should be useful in determining
> if the variable should
> be
> > included in the model....", by which they mean a
> logistic model. That is
> > to say, they certainly don't push the t test as
> being the test of choice
> > for the c-c study *because the usual objective is
> to estimate risk* (or
> > some related metric eg OR).
> >
> > There is no reference in Schlesselman's book "Case
> Control Studies" (nor
> in
> > Breslow and Day, nor in Rothman & Greenland's
> Modern Epidemiology) to the
> > use of the t test in analysis of case control
> studies, possibly
> > because (i) as I have said before, it seems to
> reverse the sense of the
> > study design and (ii) it doesn't deliver the risk
> estimate.
> >
> > So, I don't think you have analysed _incorrectly_,
> as long as your
> analyses
> > are univariate. My own preference (prejudice,
> practice and pedagogy) is
> to
> > put predictors on the right and outcomes on the
> left.
> >
> > Phil
> >
> >
> >
> > At 08:01 PM 23/03/2004 -0800, you wrote:
> > >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
>
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