Bayesian Methods for Meta-Analysis in the Presence of Publication Bias

正性和负性结果之间的发表率差异，被称为发表偏倚，在社会和行为科学中越来越受到关注。 本研究项目将开发一种新的荟萃分析方法，明确考虑到偏倚出版过程的可能性。 元分析在解释学术文献中提出的主张方面发挥了重要作用。 然而，学术期刊，特别是社会和行为科学领域的期刊，似乎更喜欢那些证明存在某种效应的手稿，而不是那些不重要的结果。 这阻碍了经典的荟萃分析方法，因为一组有偏见的经验结果的总和也会有偏见。 新方法将允许更好地汇总已发表的结果，并将提供更准确的各种实验操作和治疗效果的视图。 将开发并发布适用于各种情况的软件。本研究项目将开发一种新的荟萃分析方法，称为“统计缓解”，将行为模型与最先进的统计方法相结合。 该方法将基于贝叶斯模型平均技术，其中使用一组合理的选择模型计算效应量估计值，并在这些选择模型之间取平均值。 通过这种方法，将有可能在测量误差的噪声中隔离真实效应的信号。 研究者将在各种情况下测试该方法，在存在发表偏倚的情况下将新方法与现有方法进行比较，并进行模拟以评估该方法的有效性。 通过单一的荟萃分析方法，研究人员将能够解释发表偏倚的可能性，确认或否定零假设和非零假设，并进行效应量估计。

The differential rate of publishing between positive and negative results, which has been called publication bias, is of increasing concern in the social and behavioral sciences. This research project will develop a new approach to meta-analysis that explicitly takes into account the possibility of a biased publication process. Meta-analysis has been instrumental in interpreting the claims made in the academic literature. However, academic journals, especially in the social and behavioral sciences, seem to strongly prefer manuscripts that posit the existence of an effect rather than non-significant outcomes. This hinders classical meta-analysis methods because the aggregate of a biased set of empirical results will be biased as well. The new approach will allow for better aggregation of published results and will provide a more accurate view of the effect of various experimental manipulations and treatments. Software will be developed and published that implements this approach for a variety of situations.This research project will develop a new approach to meta-analysis called "statistical mitigation" that combines behavioral models with state-of-the-art statistical methods. The approach will be based on a Bayesian model averaging technique in which effect size estimates are computed using a set of plausible selection models and averaging across these selection models. With this approach, it will be possible to isolate the signal of true effects within the noise of measurement error. The investigator will test the method under various circumstances, compare the new approach to existing methods for inference in the presence of publication bias, and perform simulations to assess the efficiency of the method. With a single approach to meta-analysis, researchers will be able to account for the possibility of publication bias, confirm or disconfirm null and non-null hypotheses, and do effect size estimation.