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Coordinate‐based meta‐analyses (CBMA) allow researchers to combine the results from multiple functional magnetic resonance imaging experiments with the goal of obtaining results that are more likely to generalize. However, the interpretation of CBMA findings can be impaired by the file drawer problem, a type of publication bias that refers to experiments that are carried out but are not published. Using foci per contrast count data from the BrainMap database, we propose a zero‐truncated modeling approach that allows us to estimate the prevalence of nonsignificant experiments. We validate our method with simulations and real coordinate data generated from the Human Connectome Project. Application of our method to the data from BrainMap provides evidence for the existence of a file drawer effect, with the rate of missing experiments estimated as at least 6 per 100 reported. The R code that we used is available at https://osf.io/ayhfv/. [ABSTRACT FROM AUTHOR]

Methodological aspects of meta-analytic practice, heterogeneity, publication bias, metaregression and effect metric, were investigated in 14 meta-analyses reflecting major therapeutic concern in Critical Care practice. Compared with the standard Q test, the exact Zelen test was more sensitive in identifying heterogeneity. Assessment of heterogeneity impact by the I2 statistic was consistent with inferences afforded by both the Q and Zelen test. Publication bias was subject to test and metric determination: funnel plots exhibited variable asymmetry across studies and between metrics; the regression asymmetry test appeared more sensitive than the rank correlation test; the “trim and fill” method was the most sensitive, but suggested, on the basis of quantification of the effects of potentially missing studies, that meta-analyses may be resistant to such missingness. Metaregression of treatment effect against control risk using Bayesian hierarchical regression in all metrics (log odds ratio, log risk ratio and RD) suggested that naïve linear regression approaches over-diagnosed significant relationships and exhibited regression dilution. Heterogeneity, publication bias and risk related treatment effects all demonstrate estimator and metric dependence; the RD metric would appear the most capricious in this regard. [ABSTRACT FROM AUTHOR]

Background: The number of genetic association studies is increasing exponentially. Nonetheless, genetic association reports are prone to potential biases which may influence the reported outcome., Aim: We hypothesized that positive outcome for a determined polymorphism might be over-reported across genetic association studies analysing a small number of polymorphisms, when compared to studies analysing the same polymorphism together with a high number of other polymorphisms., Methods: We systematically reviewed published reports on the association of glutathione s-transferase (GST) single-nucleotide polymorphisms (SNPs) and cancer outcome., Result: We identified 79 eligible trials. Most of the studies examined the GSTM1, theGSTP1 Ile105Val mutation, and GSTT1polymorphisms (n = 54, 57 and 46, respectively). Studies analysing one to three polymorphisms (n = 39) were significantly more likely to present positive outcomes, compared to studies examining more than 3 polymorphisms (n=40) p = 0.004; this was particularly evident for studies analysing the GSTM1polymorphism (p =0.001). We found no significant associations between journal impact factor, number of citations, and probability of publishing positive studies or studies with 1-3 polymorphisms examined., Conclusions: We propose a new subtype of publication bias in genetic association studies. Positive results for genetic association studies analysing a small number of polymorphisms (n = 1-3) should be evaluated extremely cautiously, because a very large number of such studies are inconclusive and statistically under-powered. Indeed, publication of misleading reports may affect harmfully medical decision-making and use of resources, both in clinical and pharmacological development setting.