Comparing body composition between the sweet-liking phenotypes: experimental data, systematic review and individual participant data meta-analysis.

Background: Legislation aimed at reducing sugar intake assumes that sweet-liking drives overconsumption. However, evidence that a greater liking for sweet taste is associated with unhealthier body size is mixed and complicated by relatively small samples, an overreliance on body mass index (BMI) and lack of classification using sweet-liking phenotypes.

Methods: We first examined body size data in two larger samples with sweet-liking phenotyping: extreme sweet-likers, moderate sweet-likers and sweet-dislikers.

Footprint of publication selection bias on meta-analyses in medicine, environmental sciences, psychology, and economics.

Publication selection bias undermines the systematic accumulation of evidence. To assess the extent of this problem, we survey over 68,000 meta-analyses containing over 700,000 effect size estimates from medicine (67,386/597,699), environmental sciences (199/12,707), psychology (605/23,563), and economics (327/91,421). Our results indicate that meta-analyses in economics are the most severely contaminated by publication selection bias, closely followed by meta-analyses in environmental sciences and psychology, whereas meta-analyses in medicine are contaminated the least.

Teaching open and reproducible scholarship: a critical review of the evidence base for current pedagogical methods and their outcomes.

In recent years, the scientific community has called for improvements in the credibility, robustness and reproducibility of research, characterized by increased interest and promotion of open and transparent research practices. While progress has been positive, there is a lack of consideration about how this approach can be embedded into undergraduate and postgraduate research training. Specifically, a critical overview of the literature which investigates how integrating open and reproducible science may influence is needed.

Estimating the change in meta-analytic effect size estimates after the application of publication bias adjustment methods.

Publication bias poses a challenge for accurately synthesizing research findings using meta-analysis. A number of statistical methods have been developed to combat this problem by adjusting the meta-analytic estimates. Previous studies tended to apply these methods without regard to optimal conditions for each method's performance.