2006
DOI: 10.1186/1471-2288-6-50
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Development and validation of MIX: comprehensive free software for meta-analysis of causal research data

Abstract: Background: Meta-analysis has become a well-known method for synthesis of quantitative data from previously conducted research in applied health sciences. So far, meta-analysis has been particularly useful in evaluating and comparing therapies and in assessing causes of disease. Consequently, the number of software packages that can perform meta-analysis has increased over the years. Unfortunately, it can take a substantial amount of time to get acquainted with some of these programs and most contain little or… Show more

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Cited by 465 publications
(301 citation statements)
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References 20 publications
(12 reference statements)
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“…p \ 0.05 was considered representative of statistically significant publication bias [34,35]. All analyses were performed using the computer program MIX version 1.7 [36]. A p value less than 0.05 was considered statistically significant, and all the p values were two sided.…”
Section: Discussionmentioning
confidence: 99%
“…p \ 0.05 was considered representative of statistically significant publication bias [34,35]. All analyses were performed using the computer program MIX version 1.7 [36]. A p value less than 0.05 was considered statistically significant, and all the p values were two sided.…”
Section: Discussionmentioning
confidence: 99%
“…The meta-analyses were carried out using the program Meta-analysis with Interactive eXplanations (MIX, version 1.54) (Bax et al, 2006). To detect potential effects of different ancestries (European versus Asian) or study design types (case-control versus family-based), overall ORs and SEs were obtained for each group, then heterogeneity of overall ORs between different ancestries or study designs were assessed using a chi-square test with one degree of freedom, similar to the method introduced by Kazeem and Farrall (Kazeem and Farrall, 2005).…”
Section: Statistical Analysesmentioning
confidence: 99%
“…Random errors were added to the estimates, so that within-and between-study variances could be adjusted and weighted. 6 Tests of heterogeneity among studies were also performed with the chi-square test, and the extent of the heterogeneity was assessed with the I 2 statistic 39 at the outcome level. To assess for publication bias, a funnel plot was generated, in which asymmetry of the plot was interpreted to suggest that positive studies with larger samples tend to be published more readily than studies with smaller samples and/or negative (lack of statistical significance) results.…”
Section: Data Synthesis and Analysismentioning
confidence: 99%