RE: Confounders and Coincidences

["Ted Rockwell" <tedrock@CPCUG.ORG>]

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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