Novel methods to deal with publication biases: secondary analysis of antidepressant trials in the FDA trial registry database and related journal publications

Abstract

Objective To assess the performance of novel contour enhanced funnel plots and a regression based adjustment method to detect and adjust for publication biases. Design Secondary analysis of a published systematic literature review. Data sources Placebo controlled trials of antidepressants previously submitted to the US Food and Drug Administration (FDA) and matching journal publications. Methods Publication biases were identified using novel contour enhanced funnel plots, a regression based adjustment method, Egger's test, and the trim and fill method. Results were compared with a meta-analysis of the gold standard data submitted to the FDA. Results Severe asymmetry was observed in the contour enhanced funnel plot that appeared to be heavily influenced by the statistical significance of results, suggesting publication biases as the cause of the asymmetry. Applying the regression based adjustment method to the journal data produced a similar pooled effect to that observed by a meta-analysis of the FDA data.

Publication selection in health policy research: The winner's curse hypothesis

“…7 SE i is an estimate of the standard deviation of the reported effect, which usually varies greatly from one econometric estimate to the next. 8 However, we find clear evidence of publication bias among the 50 RCTs of antidepressants that are published (t=5.47; p< .001)-also see (Turner et al, 2008;Moreno et al, 2009b). 9 The conventional Q-test for heterogeneity is significant at any level (χ 2 (163) =19,272).…”

“…showing clear signs of being selected to be positive and statistically significant (Turner et al, 2008;Moreno et al, 2009b).…”

Demand for gasoline is more price-inelastic than commonly thought

“…Thus, on average, both the short-and long-run price elasticity of gasoline demand is statistically different from zero even after correcting for publication bias. To estimate the true average elasticity precisely, we need to employ the Heckman meta-regression proposed by Stanley & Doucouliagos (2007) and corroborated by Moreno et al (2009). This is achieved by estimating regression (4); the results are reported in Table 4.…”

“…To examine the magnitude of the true elasticity, Stanley & Doucouliagos (2007) recommends an augmented version of (3); this specification is also supported as the best method to correct for publication bias by a survey of meta-analysis methods published in the British Medical Journal (Moreno et al, 2009). The specification is based on the assumption that the relation between standard errors and publication bias in (1) is quadratic; the model is called the Heckman metaregression (see Stanley & Doucouliagos, 2007, for details).…”

Publication Bias in the Organizational Sciences

“…Separate from the meta-analytic estimation model, the trim and fill analysis can be conducted using either a fixed-or a random-effects model. Generally, the fixed-effects model for the trim and fill process is recommended (Moreno, Sutton, Turner, et al, 2009;Sutton, 2005;Terrin et al, 2003) because research suggests that the random-effects model incorrectly adjusts asymmetry in the distribution of samples (Terrin et al, 2003) and gives too much weight to imprecise samples compared to the fixed-effects model (Sutton, 2005). In addition to the estimation method, one can also use an L or R estimator for the number of missing samples (for detailed description of the estimators, see Duval & Tweedie, 2000a, 2000b.…”