We will convene for the Applied Statistics Workshop (Gov 3009) TOMORROW 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