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RE: st: ORs for non-rare outcomes


From   "Hansell, Anna L" <a.hansell@imperial.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: ORs for non-rare outcomes
Date   Thu, 8 Apr 2004 11:39:05 +0100

For some types of case-control study design it should be appropriate to obtain estimates of relative risk or relative rate for common diseases.  I suggest reading the paper by "Case-Control Designs in the Study of Common Diseases: Updates on the demise of the rare disease assumption and the choice of sampling schemes for controls" by Laura Rodrigues and Betty Kirkwood in the International Journal of Epidemiology 1990;19(1):205-213.  I have never used this myself, but the paper is set out very clearly.

Best wishes
Anna Hansell
Imperial College London


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Marcello
Pagano
Sent: 08 April 2004 11:25
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: ORs for non-rare outcomes


Some have this aversion to odds which is difficult to understand. Odds 
are just as natural as probabilities, ask anyone at a race track. 
Indeed, as someone pointed out, here is a sector of society not known 
for its intelligence, punters, who understand odds thoroughly, but 
health scientists, some even pursuing a Nobel, for some reason cannot 
understand odds?  It does not compute.

This is especially troublesome with case control studies because odds 
ratios are estimable but relative risks are not.  This is inherent in 
the design of a case control study, something that is immutable.

Someone made the observation that for rare diseases the OR and the RR 
are approximately equal. Unfortunately, this crutch has had the stunting 
effect of not dealing with odds ratios as the natural measure that they are.

Make odds even with risk!

m.p.



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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