Dear workshop community,

We will convene for the Applied Statistics Workshop (Gov 3009) next week on Wednesday (2/6).

The speaker is Maya Mathur (Harvard Epidemiology) who will be presenting her work "Sensitivity analysis for publication bias and selective reporting in meta-analysis."

Where: CGIS Knafel Building, Room K354 (see this link for directions).

When: Wednesday, February 6th at 12 noon - 1:30 pm.

Abstract: We propose sensitivity analyses for selection in meta-analysis due to publication bias, selective reporting, and "p-hacking". We consider a publication process such that "statistically significant'' positive results are more likely to be published than negative or "nonsignificant'' results by an unknown ratio. Using inverse-probability weighting and robust estimation that accommodates non-normal true effects, small meta-analyses, and clustering, we develop sensitivity analyses that enable statements such as: "For publication bias to shift the observed point estimate to the null, 'significant' positive results would need to be at least 30-fold more likely to be published than negative or 'nonsignificant' results.'' Comparable statements can be made regarding shifting to a chosen non-null value or shifting the confidence interval. We show that a worst-case meta-analytic point estimate under maximal publication bias can be obtained simply by conducting a standard meta-analysis of only the negative and "nonsignificant'' studies; this method sometimes indicates that no amount of publication bias could "explain away'' the results. We illustrate the proposed methods using real-life meta-analyses. An R package is forthcoming.

All are welcome! Lunch is provided!

Best,

Connor Jerzak
Applied Statistics Workshop -- Graduate Student Coordinator
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