You could try glm with family binomial and a log link instead of logit.
This will give you risk ratios.
Michael Hills
On Thu, 2004-04-08 at 10:17, roger webb wrote:
> Dear Statalist,
>
> I�d be grateful for any comments concerning the interpretation of
> odds ratio in situations when the outcome is not rare.
>
> 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 outcome
> 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 poor
> outcome treated as cases and those that have good outcome
> 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 among
> 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
> assumption.
>
> Does anyone know of any alternative modelling strategies
> (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 overestimate
> relative risks in this data set?
>
>
> Thanks in advance.
>
> Roger Webb
> University of Manchester (UK)
>
>
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