"Publication bias" is a relatively new statistical phenomenon that
only arises when one attempts through a meta-analysis to review all studies,
significant or insignificant, in order to provide a total perspective on a
particular issue. This has recently received some notoriety as an issue in the
evaluation of the relative risk of lung cancer associated with passive smoking,
following legal challenges to a 1992 Environmental Protection Agency analysis
which concluded that such exposure is associated with significant excess risk
of lung cancer.
¶ We introduce a Bayesian approach which estimates and adjusts for
publication bias. Estimation is based on a data-augmentation principle within a
hierarchical model, and the number and outcomes of unobserved studies are
simulated using Gibbs sampling methods. This technique yields a quantitative
adjustment for the passive smoking meta-analysis. We estimate that there may be
both negative and positive but insignificant studies omitted, and that failing
to allow for these would mean that the estimated excess risk may be overstated
by around 30%, both in U.S. studies and in the global collection of
studies.
Publié le : 1997-11-14
Classification:
Meta-analysis,
publication bias,
missing data,
data augmentation,
Markov chain Monte Carlo,
MCMC,
Gibbs sampling,
environmental tobacco smoke,
ETS,
passive smoking,
lung cancer,
file-drawer problem
@article{1030037958,
author = {Givens, Geof H. and Smith, D. D. and Tweedie, R. L.},
title = {Publication bias in meta-analysis: a Bayesian data-augmentation
approach to account for issues exemplified in the passive smoking debate},
journal = {Statist. Sci.},
volume = {12},
number = {1},
year = {1997},
pages = { 221-250},
language = {en},
url = {http://dml.mathdoc.fr/item/1030037958}
}
Givens, Geof H.; Smith, D. D.; Tweedie, R. L. Publication bias in meta-analysis: a Bayesian data-augmentation
approach to account for issues exemplified in the passive smoking debate. Statist. Sci., Tome 12 (1997) no. 1, pp. 221-250. http://gdmltest.u-ga.fr/item/1030037958/