Article in Epidemiology

["Jacobus, John (OD/ORS)" <jacobusj@ors.od.nih.gov>]

I thought this might be of interest.

-- John



John Jacobus, MS

Certified Health Physicist 

3050 Traymore Lane

Bowie, MD  20715-2024



E-mail:  jenday1@email.msn.com (H)      



EPIDEMIOLOGY 2001;12:114-122



Title:  Causation of Bias: The Episcope   



Authors:  Malcolm Maclure1; Sebastian Schneeweiss2



>From the 1Department of Epidemiology, Harvard School of Public Health, and

2Harvard Medical School and Division of Pharmacoepidemiology and

Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA.



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Abstract:  A risk ratio or difference from a meta-analysis is as many as ten

steps away from the unobservable causal risk ratios and differences in

target populations. The steps are like lenses, filters, or other fallible

components of the epidemiologist's "telescope" for observing populations.

Each step is another domain where different biases can be caused. How biases

combine across domains in the production of epidemiologic evidence can be

quickly explained to nonepidemiologists by using a sequence of causal arrow

diagrams with easy notation: (a) agent of interest, (b) background risk

factors, (c) correlated causes, (d) diagnosis, (e) exposure measurement, (f)

filing of data, (g) grouping of cohorts, (h) harvesting of cases and

controls, (i) interpretations of investigators, (j) judgments of journals,

and (k) knowledge of meta-analysts. For epidemiologists, this article serves

as a review of ideas about confounding, information bias, and selection bias

and underscores the need for routinely analyzing the sensitivity of study

findings to multiple hypothesized biases.



Keywords: bias; meta-analysis; causal inference; epidemiologic methods;

confounding.





-- John 

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