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RE: Confounders and Coincidences
Ted "hits the nail on the head" in this message. My research was
in basic physics for 25 years before I went into Health Physics. I was
primarily an experimentalist, but I wrote many papers in theoretical
physics and I associated closely with theoretical physicists both socially
and professionally. I can assure you that theoretical physicists use
numerical examples at every step of their reasoning. The usual procedure
is to think things through with numerical examples and, for publication,
dress it up with fancy mathematics. If you ask a theoretical physicist
about an issue, he will provide you with a numerical example in a minute
or two.
Bernard L. Cohen
Physics Dept.
University of Pittsburgh
Pittsburgh, PA 15260
Tel: (412)624-9245
Fax: (412)624-9163
e-mail: blc@pitt.edu
web site: www.phyast.pitt.edu/~blc
On Thu, 8 May 2003, Ted Rockwell wrote:
> Please help out a simple non-epidemiologist. If it is so easy for a
> confounder to reverse a curve such as Cohen's, then why is no one willing to
> make up a plausible example, with numbers, that will simply do that? That's
> what he has repeatedly asked for, and all he gets is generic speculation
> that such a thing is possible. It's possible that the phase of the moon
> could do it, too, but we won't really know until someone puts in some
> numbers and it checks out.
>
> Ted Rockwell
>
> -----Original Message-----
> From: owner-radsafe@list.vanderbilt.edu
> [mailto:owner-radsafe@list.vanderbilt.edu]On Behalf Of epirad@mchsi.com
> Sent: Thursday, May 08, 2003 12:34 PM
> To: Otto G. Raabe
> Cc: radsafe@list.vanderbilt.edu
> Subject: Re: Confounders and Coincidences
>
>
> Dear Otto,
>
> Yes, using ecologic data to control confounding is helpful sometimes. In
> fact, it does help especially if there are not large inter and intra county
> non linear effects. However, we know without question that these non linear
> sources of confounding and effect modification exist in Dr. Cohen's data.
>
> Two papers really do a nice job of adressing this problem.
>
> Ecological bias, confounding, and effect modification
>
> S Greenland and H Morgenstern
>
> Division of Epidemiology, UCLA School of Public Health 90024.
>
> Ecological bias is sometimes attributed to confounding by the group variable
> (ie the variable used to define the ecological groups), or to risk factors
> associated with the group variable. We show that the group variable need not
> be a confounder (in the strict epidemiological sense) for ecological bias to
> occur: effect modification can lead to profound ecological bias, whether or
> not the group variable or the effect modifier are independent risk factors.
> Furthermore, an extraneous risk factor need not be associated with the study
> variable at the individual level in order to produce ecological bias. Thus
> the
> conditions for the production of ecological bias by a covariate are much
> broader than the conditions for the production of individual-level
> confounding
> by a covariate. We also show that standardization or ecological control of
> variables responsible for ecological bias are generally insufficient to
> remove
> such bias.
>
> --------------------------------------
> Ecologic versus individual-level sources of bias in ecologic estimates of
> contextual health effects
>
> Sander Greenland
>
> Department of Epidemiology, UCLA School of Public Health, and Department of
> Statistics, UCLA College of Letters and Science, 22333 Swenson Drive,
> Topanga,
> CA 90290, USA.
>
> Bill's insert - (Does this first sentence sound familiar?)
>
> Abstract:
>
> A number of authors have attempted to defend ecologic (aggregate) studies by
> claiming that the goal of those studies is estimation of ecologic
> (contextual
> or group-level) effects rather than individual-level effects. Critics of
> these
> attempts point out that ecologic effect estimates are inevitably used as
> estimates of individual effects, despite disclaimers. A more subtle problem
> is
> that ecologic variation in the distribution of individual effects can bias
> ecologic estimates of contextual effects. The conditions leading to this
> bias
> are plausible and perhaps even common in studies of ecosocial factors and
> health outcomes because social context is not randomized across typical
> analysis units (administrative regions). By definition, ecologic data
> contain
> only marginal observations on the joint distribution of individually defined
> confounders and outcomes, and so identify neither contextual nor individual-
> level effects. While ecologic studies can still be useful given appropriate
> caveats, their problems are better addressed by multilevel study designs,
> which obtain and use individual as well as group-level data. Nonetheless,
> such
> studies often share certain special problems with ecologic studies,
> including
> problems due to inappropriate aggregation and problems due to temporal
> changes
> in covariate distributions.
>
> Regards, Bill
> bill-field@uiowa.edu
> > At 03:52 PM 5/8/03 +0000, Bill Field wrote:
> > >Dr. Cohen wrote -
> > >
> > >Unfortunately, ecological control of a covariate contributing to ecologic
> > bias
> > >is generally inadequate to remove the bias produced by the covariate even
> in
> > >the absence of measurement error.
> > ****************************************************
> > May 8, 2003
> >
> > Dear Bill:
> >
> > Thanks for your comment. You bring up an important point.
> >
> > But it seems to me that the fact that ecological control may not remove
> the
> > bias does not mean that it never helps. If all you have is ecological
> data,
> > isn't testing the effect of ecological control a useful exercise?
> >
> > Otto
> >
> > **********************************************
> > Prof. Otto G. Raabe, Ph.D., CHP
> > Center for Health & the Environment
> > (Street Address: Bldg. 3792, Old Davis Road)
> > University of California, Davis, CA 95616
> > E-Mail: ograabe@ucdavis.edu
> > Phone: (530) 752-7754 FAX: (530) 758-6140
> > ***********************************************
>
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