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

存在发表偏倚的贝叶斯荟萃分析方法

• 批准号: 1534472
• 负责人: Joachim Vandekerckhove
• 金额: $ 26万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1534472&HistoricalAwards=false
• 关键词: Bayesian; Methods; Meta; Analysis; Presence

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.

正面和负面结果之间的发表率差异（称为发表偏差）在社会科学和行为科学中越来越受到关注。 该研究项目将开发一种新的荟萃分析方法，明确考虑发表过程中存在偏见的可能性。 荟萃分析有助于解释学术文献中的主张。 然而，学术期刊，尤其是社会科学和行为科学领域的学术期刊，似乎更喜欢假设存在效果而不是不显着结果的手稿。 这阻碍了经典的荟萃分析方法，因为一组有偏见的实证结果的汇总也会有偏见。 新方法将允许更好地汇总已发表的结果，并将更准确地了解各种实验操作和治疗的效果。 将开发和发布在各种情况下实施这种方法的软件。该研究项目将开发一种称为“统计缓解”的新元分析方法，该方法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并对这些选择模型进行平均来计算效应大小估计。 通过这种方法，可以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的推理方法进行比较，并进行模拟以评估该方法的效率。 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，确认或否定无效和非无效假设，并进行效应大小估计。