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Assessing heterogeneity in metaanalysis: Q statistic or I2 index? Psychol Methods
"... In metaanalysis, the usual way of assessing whether a set of single studies is homogeneous is by means of the Q test. However, the Q test only informs metaanalysts about the presence versus the absence of heterogeneity, but it does not report on the extent of such heterogeneity. Recently, the I 2 ..."
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In metaanalysis, the usual way of assessing whether a set of single studies is homogeneous is by means of the Q test. However, the Q test only informs metaanalysts about the presence versus the absence of heterogeneity, but it does not report on the extent of such heterogeneity. Recently, the I 2 index has been proposed to quantify the degree of heterogeneity in a metaanalysis. In this article, the performances of the Q test and the confidence interval around the I 2 index are compared by means of a Monte Carlo simulation. The results show the utility of the I 2 index as a complement to the Q test, although it has the same problems of power with a small number of studies.
Publication Bias in MetaAnalysis: A Bayesian DataAugmentation Approach to Account for Issues Exemplified in the Passive Smoking Debate
 Statistical Science
, 1997
"... `Publication bias' is a relatively new statistical phenomenon that only arises when one attempts through a metaanalysis 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 ev ..."
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`Publication bias' is a relatively new statistical phenomenon that only arises when one attempts through a metaanalysis 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 EPA 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 metaanalysis. We estimate that there may be both negative and positive but insignificant studies omitted, and that failing to allow for these woul...
A Nonparametric "Trim and Fill" Method of Assessing Publication Bias in Metaanalysis
"... Metaanalysis collects and synthesizes results from individual studies to estimate an overall effect size. If published studies are chosen, say through a literature review, an inherent selection bias may arise, since for example, studies may tend to be published more readily if they are statisticall ..."
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Cited by 7 (2 self)
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Metaanalysis collects and synthesizes results from individual studies to estimate an overall effect size. If published studies are chosen, say through a literature review, an inherent selection bias may arise, since for example, studies may tend to be published more readily if they are statistically significant, or deemed to be more `interesting' in terms of the impact of their outcomes. We develop a simple rankbased data augmentation technique, formalizing the use of funnel plots, to estimate and adjust for the numbers and outcomes of missing studies. Several nonparametric estimators are proposed for the number of missing studies, and their properties are developed analytically and through simulations. We apply the method to simulated and epidemiological data sets, and show it is both effective and consistent with other criteria in the literature. Corresponding author's email address: tweedie@stat.colostate.edu Key words: Metaanalysis; Publication bias; Missing studies; File dra...
Bayesian Random Effects MetaAnalysis of Trials with Binary Outcomes: Methods for the absolute risk difference and relative risk scales
, 2001
"... When conducting a metaanalysis of clinical trials with binary outcomes, a normal approximation for the summary treatment effect measure in each trial is inappropriate in the common situation where some of the trials in the metaanalysis are small, or the observed risks are close to 0 or 1. This ..."
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Cited by 6 (1 self)
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When conducting a metaanalysis of clinical trials with binary outcomes, a normal approximation for the summary treatment effect measure in each trial is inappropriate in the common situation where some of the trials in the metaanalysis are small, or the observed risks are close to 0 or 1. This problem can be avoided by making direct use of the binomial distribution within trials. A fully Bayesian method has already been developed for random effects metaanalysis on the logs odds scale using the BUGS implementation of Gibbs sampling. In this paper, we demonstrate how this method can be extended to perform analyses on both the absolute and relative risk scales.
Trim and Fill: A Simple Funnel Plot Based Method of Testing and Adjusting for Publication Bias in Metaanalysis
"... this paper we investigate three properties of this approach: (a) We consider which of the estimators have the better mean square error (MSE) properties: we show in Section 3 that the estimators R 0 and L 0 are both better than Q 0 , but that there are values of the number of observed and missing stu ..."
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this paper we investigate three properties of this approach: (a) We consider which of the estimators have the better mean square error (MSE) properties: we show in Section 3 that the estimators R 0 and L 0 are both better than Q 0 , but that there are values of the number of observed and missing studies for which each is better than the other; (b) We use the distributional properties of the estimators to formulate tests for the existence of publication bias, and compare these with recent methods of Begg (1994) and Egger et al (1997): the resulting tests, in Section 5, appear to be quite powerful if there are more than 56 missing studies; (c) We compare the properties of the iterated version of the algorithm (used in practice) with those of the analytic versions in Taylor and Tweedie (1998): using simulations, we see in Section 4.2 that the iteration does not adversely affect accuracy in general, and so the analytic descriptions of the tests and estimators can be used in the iteration. We then apply the iterative algorithm and the test methods, in Section 6, to a number of sets of studies, and show that they are consistent with the Begg (1994) and Egger et al (1997) approaches, and also to the more complex Bayesian method in Givens et al (1997). These data sets include metaanalyses of the risk of Chlamydia trachomatis from oral contraceptive (OC) use, described by Cottingham and Hunter (1992) and analyzed by Begg (1994); studies of the association between lung cancer and passive smoking, collected and analyzed in Tweedie et al (1996); and a psychometric metaanalysis of IQ scores and teacher expectancy, collected by Raudenbush (1984), and analyzed by Begg (1994) and Gleser and Olkin (1996). 1.2 Funnel plots and quantitative methods
Positive Estimation Of The BetweenGroup Variance Component In OneWay Anova And MetaAnalysis
"... Positive estimators of the betweengroup (betweenstudy)... ..."
Testing for Homogeneity in MetaAnalysis I. The One Parameter Case: Standardized Mean Difference
, 906
"... Metaanalysis seeks to combine the results of several experiments in order to improve the accuracy of decisions. It is common to use a test for homogeneity to determine if the results of the several experiments are sufficiently similar to warrant their combination into an overall result. Cochran’s Q ..."
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Metaanalysis seeks to combine the results of several experiments in order to improve the accuracy of decisions. It is common to use a test for homogeneity to determine if the results of the several experiments are sufficiently similar to warrant their combination into an overall result. Cochran’s Q statistic is frequently used for this homogeneity test. It is often assumed that Q follows a chisquare distribution under the null hypothesis of homogeneity, but it has long been known that this asymptotic distribution for Q is not accurate for moderate sample sizes. Here we present formulas for the mean and variance of Q under the null hypothesis which represent O(1/n) corrections to the corresponding chisquare moments in the one parameter case. The formulas are fairly complicated, and so we provide a program (available at
RESEARCH ARTICLE Open Access
"... P53 in human melanoma fails to regulate target genes associated with apoptosis and the cell cycle and may contribute to proliferation ..."
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P53 in human melanoma fails to regulate target genes associated with apoptosis and the cell cycle and may contribute to proliferation
RESEARCH ARTICLE
"... A random effects variance shift model for detecting and accommodating outliers in metaanalysis ..."
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A random effects variance shift model for detecting and accommodating outliers in metaanalysis
Constructing Appropriate Models for MetaAnalyses
, 2009
"... A copy can be downloaded for personal noncommercial research or ..."