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Adrian Smith
02-19-2008, 07:15 PM
May be relevant:

Jim Giles interviews John Ioannidis for "New Scientist"

16th Feb 2008, pp.44-45 (Vol. 197)



"When the clinical epidemiologist John Ioannidis published a paper
entitled "Why most published research findings are false" in 2005, he
made a lot of scientists very uncomfortable. The study was the result of
15 years' work cataloguing the factors that plague the interpretation of
scientific results, such as the misuse of statistics or poor
experimental design"

Title: Why most published research findings are false
Author(s): Ioannidis, JPA
Source: PLOS MEDICINE Volume: 2 Issue: 8 Pages: 696-701
Published: AUG 2005

"Some colleagues and I have looked at high-profile papers, with over
1000 citations each, that were later completely contradicted by large,
well-conducted studies. One example is the finding that beta-carotene
protects against cancer. It doesn't, but we found a sizeable component
of literature where these original beliefs were still supported. It's
hard to believe the researchers had never heard they had been refuted.

Jim Giles: "How should we promote the studies that produce more credible
results, rather than those that are simply statistically significant?"

"There are several ways to do this. One: do larger, well-designed
studies. Two: instead of having 10 teams of researchers, each working
behind closed doors, investigators should collaborate and study the same
questions. All the data should be made publicly available. If one team
comes up with an interesting result then the whole consortium should try
to replicate it. Much of the work I've been doing for the past 10 years
has been about creating consortia to carry out research. The experience
has been very positive.

>From issue 2643 of "New Scientist" magazine, 16 February 2008, page
44-45

Title: Why most published research findings are false: Author's reply to
Goodman and Greenland
Author(s): Ioannidis, JPA
Source: PLOS MEDICINE Volume: 4 Pages: 1132-1133 Published: 2007

Title: Limitations are not properly acknowledged in the scientific
literature
Author(s): Ioannidis, JPA
Source: JOURNAL OF CLINICAL EPIDEMIOLOGY Volume: 60 Issue: 4
Pages: 324-329 Published: APR 2007

Title: Why most published research findings are false: Problems in the
analysis
Author(s): Goodman, S; Greenland, S
Source: PLOS MEDICINE Volume: 4 Issue: 4 Pages: 773-773
Published: APR 2007

Title: Most published research findings are false- but a little
replication goes a long way
Author(s): Moonesinghe, R; Khoury, MJ; Janssens, ACJW
Source: PLOS MEDICINE Volume: 4 Issue: 2 Pages: 218-221
Published: FEB 2007

Title: THE DISSEMINATION OF FALSE DATA THROUGH INADEQUATE CITATION
Author(s): LARSSON, KS
Source: JOURNAL OF INTERNAL MEDICINE Volume: 238 Issue: 5 Pages:
445-450 Published: NOV 1995

--------

Adrian Smith
Leeds University Library
+44 (0)113 3435531


-----Original Message-----
From: * Biomechanics and Movement Science listserver
[mailto:BIOMCH-L@NIC.SURFNET.NL] On Behalf Of McFarlane, David
Sent: 19 February 2008 22:36
To: BIOMCH-L@NIC.SURFNET.NL
Subject: [BIOMCH-L] What criteria should we use for interpreting
epidemiology?

Dear all,

Lately in my professional reading I have noticed that the standards of
evidence varies greatly between different fields. For instance, the
mode of establishing causality used in science (which was devised by
Jakob Henle and Robert Koch for research in bacteriology) requires that
in every single instance the effect follows the cause. By contrast
epidemiology uses a system of causal inference that is based on the
philosophies of David Hume and John Stuart Mill (Morabia, 2005). The
question of identifying causes in epidemiology has always been a subject
of controversy. For instance, the controversy over the interpretation of
the statistical relationship between smoking and lung cancer caused a
landmark debate in that field in the second half of the twentieth
century. It led Bradford Hill to formulate the "pragmatics" of risk
factor epidemiology in 1965 (Berlivet, 2005). His model for establishing
causation ("The Hill causation model") is well known in the public
health field and widely used. For example Hill's nine proposed
"criteria" for determining causation were used was used to classify
Chrysotile asbestos as a cause of mesothelioma (Lemen, 2004); it met all
nine of them. However, "multiple causation" is the canon of contemporary
epidemiology and its "web of causation" is widely accepted though it is
a very poorly elaborated model (Krieger, 1994). Sadly public heath
debates these days are often based on evidence that use it in
questionable ways and this topic clearly deserves further debate. Has
anyone found a good review of criteria currently in use for interpreting
causation?

Regards,

David McFarlane MAppSc (Ergonomics)
Ergonomist, WorkCover NSW

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