2020-04-21
Accumulation (Bias) in meta-analysis
Publication
Publication
Studies accumulate over time and meta-analyses are mainly retrospective. These two characteristics introduce dependencies between the analysis time, at which a series of studies is up for meta-analysis, and results within the series. Dependencies introduce bias —Accumulation Bias— and invalidate the sampling distribution assumed for p-value tests, thus inflating type-I errors. But dependencies are also inevitable, since for science to accumulate efficiently, new research needs to be informed by past results.
In this KEBB webinar, I would like to discuss why it is important to think about accumulation in meta-analysis. And explain how a new approach to statistics that we call 'safe' and 'any-time valid', could help us handle the problem of accumulation bias, thanks to its connection to the likelihood paradigm and game-theory.
Additional Metadata | |
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AMC KEBB Seminar | |
Organisation | Machine Learning |
ter Schure, J. (2020). Accumulation (Bias) in meta-analysis. |
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30339.pdf Presentation , 1mb |