Download Tu et al. Respond to “Barker Meets Simpson”

American Journal of Epidemiology
Copyright © 2005 by the Johns Hopkins Bloomberg School of Public Health
All rights reserved
Vol. 161, No. 1
Printed in U.S.A.
DOI: 10.1093/aje/kwi004
Tu et al. Respond to “Barker Meets Simpson”
Yu-Kang Tu1,2, George T. H. Ellison3, Robert West1, and Mark S. Gilthorpe1
1
Biostatistics Unit, Centre for Epidemiology and Biostatistics, University of Leeds, Leeds, United Kingdom.
Leeds Dental Institute, University of Leeds, Leeds, United Kingdom.
3 St. George’s Hospital Medical School, London, United Kingdom.
2
Received for publication September 22, 2004; accepted for publication September 28, 2004.
Weinberg (1) highlights the challenges and pitfalls faced
when statistically analyzing data from observational
(nonrandomized) studies to explore causal hypotheses. She
reminds us that statistically significant associations between
variables in such studies can be ambiguous. Extreme caution
is required when statistical modeling of data from observational studies is used to infer causality from statistical associations between exposure and outcome variables. When
knowledge regarding the relation between exposure(s) and
potential confounder(s) is incomplete, no causal inference
should be made based on such data alone. Indeed, under
these circumstances, it is questionable whether one can
invoke a statistical artifact (as we have done) without reference to an accepted causal model. This is why, when demonstrating a serious potential flaw in the statistical analyses of
observational data believed to support the fetal origins of
adult disease hypothesis, our article (2) invoked a widely
accepted causal model linking adult blood pressure, birth
weight, and current weight.
Using our simple model, we demonstrated one reason why
the statistical relation between birth weight and adult blood
pressure should not be adjusted for current weight if this is
not a true confounder (2). In Weinberg’s more complex
“causal” model (1), which she describes in the form of a
directed acyclic graph and which includes prenatal environmental and genetic factors (set A factors) that influence both
birth weight and current weight, adjustment for current
weight can be justified provided there is no direct causal
pathway from birth weight to current weight. However, if set
B factors (which affect current weight and blood pressure)
and/or set C factors (which affect current weight, blood pressure, and birth weight) are also present or there is a causal
pathway from birth weight to current weight, adjustment for
current weight remains inappropriate. These last two
scenarios seem more likely than the first simply because, like
any analytical variable, birth weight is both a correlate of and
a proxy for a range of other variables. Like any biologic
entity, birth weight is both a consequence and a cause of
other biologic phenomena; like any phenotype, birth weight
is the product of the interaction between genetic and environmental factors. Thus, while many studies exploring the
fetal origins of adult disease hypothesis use birth weight as a
marker for prenatal factors responsible for size at birth, birth
weight is also a variable that captures those other genetic and
environmental phenomena with which it is associated,
directly and indirectly, causally and by chance. The problem
is therefore this: What is the correct causal graph to represent
reality?
Weinberg’s directed acyclic graph (1) is one of many
possible causal models linking prenatal factors, birth weight,
current weight, and blood pressure. Although some causal
models seem more plausible than others, it is crucial to
acknowledge that several feasible models exist. For proponents of the fetal origins of adult disease hypothesis, current
weight is a “confounder” because there is at least a possibility (in one or more potential causal models) that adjustment for current weight is justified. Until we have clearer
evidence from experimental studies (3) and a greater understanding of the complex anatomic, physiologic, and
biochemical processes linking birth weight, current weight,
and blood pressure (4), all causal models will be subject to
debate. Epidemiologic studies need to be more transparent
and rigorous in their reports of the formulation of research
questions, the underlining biologic mechanism(s), and the
statistical testing of specific research hypotheses (5, 6).
Whatever the preferred causal model, researchers into the
fetal origins of adult disease need to be aware that adjustment for variables, such as current weight, is subject to the
reversal (Simpson) paradox. Otherwise, old paradoxes never
die.
REFERENCES
1. Weinberg C. Invited commentary: Barker meets Simpson. Am
J Epidemiol 2005;161:33–35.
Correspondence to Dr. Yu-Kang Tu, Biostatistics Unit, Centre for Epidemiology and Biostatistics, University of Leeds, 30/32 Hyde Terrace,
Leeds, LS2 9LN, United Kingdom (e-mail: [email protected]).
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Am J Epidemiol 2005;161:36–37
Tu et al. Respond to “Barker Meets Simpson” 37
2. Tu YK, West R, Ellison ETH, et al. Why evidence for the fetal
origins of adult disease might be a statistical artifact: the “reversal paradox” for the relation between birth weight and blood
pressure in later life. Am J Epidemiol 2005;161:27–32.
3. Ceesay SM, Prentice AM, Cole TJ, et al. Effects on birth weight
and perinatal mortality of maternal dietary supplements in rural
Gambia: 5 year randomised controlled trial. BMJ 1997;315:
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Am J Epidemiol 2005;161:36–37
4. Gluckman PD, Hanson MA. The developmental origins of the
metabolic syndrome. Trends Endocrinol Metab 2004;15:183–7.
5. Huxley RR, Neil A, Collins R. Unravelling the fetal origins
hypothesis: is there really an inverse association between birthweight and subsequent blood pressure? Lancet 2002;360:659–
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6. Lucas A, Fewtrell M, Cole TJ. Fetal origins of adult disease—
the hypothesis revisited. BMJ 1999;319:245–9.