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Re: st: ORs for non-rare outcomes
Rothman, Modern Epidemiology Second Edition, pages 93
states: "in general the rare-disease assumption is not
needed in case control studies, except of the
cumulative type". The cumulative type means that
controls are sampled from "that portion of the
population that remains after eliminating accumulated
cases". The odds ratio will be an estimate of the
Relative Risk (IRR or CIR) when controls are selected
using density sampling. I'm not sure how this relates
to your situation, but it would be worth reading over
this section, or some other related papers and
considering how your cases and controls were selected
in relation to the underlying source population.
--- roger webb <email@example.com> wrote:
> Dear Statalist,
> I’d be grateful for any comments concerning the
> interpretation of
> odds ratio in situations when the outcome is not
> I am investigating the predictors of ‘significant
> parenting problems’
> in a sample of women (n=239) admitted for inpatient
> treatment for
> schizophrenia immediately following childbirth. The
> variable is coded in a binary fashion and poor
> outcome is common
> in this sample (i.e. 50% of the women).
> So far my strategy has been to analyse the data as
> if they were
> from a case-control study, with the mothers who have
> outcome treated as cases and those that have good
> treated as controls. I have used logistic regression
> as I wish to
> generate multivariate models.
> In a univaraite model I have a binary coded
> explanatory variable
> (‘mother has a partner with psychiatric illness’:
> ‘Yes’ vs. ‘No’).
> Calculating the exposure odds ratio, 38.5% of the
> ‘cases’ have a
> partner who is ill compared with 7% of the
> ‘controls’ (OR=8.1).
> However, if I compare the prevalence of poor outcome
> mothers with ill partners (82%) against those
> without ill partners
> (36%) the risk ratio is considerably lower (RR=2.3).
> (Here is the cross-tabulation from which I
> calculated the OR/RR):
> Case (+) Control (-)
> Exposed (+) 37 8
> Unexposed (-) 59 103
> I presume that the considerable discrepancy between
> the OR and
> RR has occurred due to an extreme violation of the
> rare disease
> Does anyone know of any alternative modelling
> (preferably that can implemented in Stata) that
> would enable me to
> estimate relative risks with covariate adjustment
> with a commonly
> occurring binary outcome variable?
> Alternatively, would it be appropriate to proceed
> with logistic
> regression but state that the odds ratios grossly
> relative risks in this data set?
> Thanks in advance.
> Roger Webb
> University of Manchester (UK)
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