http://nova.newcastle.edu.au/vital/access/services/Feed ${session.getAttribute("locale")} 5 http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:7108 Although there has been a rapid rise in the publication of meta-analyses of genetic association studies, little is known about their methodological quality. The authors reviewed the quality of 120 randomly selected genetic meta-analyses published between 2005 and 2007. Data extracted included issues of general relevance and other issues specific to genetic epidemiology. Quality was markedly poorer in the 26% of the meta-analyses that accompanied a report on a primary study. Such meta-analyses were predominantly published in specialist journals, and their quality was positively associated with the impact factor of the journal. Among the meta-analyses that did not accompany a primary study, Human Genome Epidemiology reviews tended to score better than the others, although the comparison was limited by relatively small numbers. Comparison of the overall quality with that of genetic meta-analyses published before 2000 showed improvement in both conduct and reporting. However, the quality of the handling of specific genetic issues remains disappointingly low. For a few key general quality issues, the authors compared their findings with findings in other fields of medicine and found that general quality was similar. On the basis of this review, the authors provide practical recommendations for the conduct and reporting of genetic meta-analyses. 2011-02-02T22:40:20.060Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:4619 Background: It is often recommended that control groups in meta-analyses of genetic association studies are checked for Hardy-Weinberg equilibrium (HWE) as a surrogate for assessing study quality. However, tests for HWE have low power and there is currently no consensus about how to handle studies that deviate significantly from HWE. Methods: We identified 72 papers describing 114 meta-analyses of 1603 primary gene–disease comparisons. Based on these studies andon related simulations, we evaluated four different strategies for handling studies that appear not to be in HWE: (i) include them in the meta-analysis; (ii) exclude them if the test for HWE results in P<0.05; (iii) exclude them if a measure of the size of departure from HWE is large and (iv) exclude them if (ii) and (iii). Results: Of the 72 papers, 26 did not report information on HWE, with a trend toward increased reporting with time. HWE was evaluated through testing, with only three papers assessing the size of departure. On re-analysis, 9% of the 1603 primary comparisons showed significant deviation from HWE. The chance of an extreme departure from HWE was inversely related to the sample size of the study. Simulations suggest that there is no advantage in excluding studies that appear not to be in HWE. Conclusions: Meta-analyses should report both the magnitude and the statistical significance of departures from HWE. Studies that appear to deviate from HWE should be investigated further for weaknesses in theirdesign, but these studies should not be excluded unless there are other grounds for doubting the quality of the study. 2010-04-27T05:10:22.357Z ]]>