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Re: st: ORs for non-rare outcomes


From   Michael Hills <mhills@blueyonder.co.uk>
To   Statalist <statalist@hsphsun2.harvard.edu>
Subject   Re: st: ORs for non-rare outcomes
Date   08 Apr 2004 10:52:47 +0100

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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