[Turing-Southampton] S3RI seminar: Leonhard Held (University of Zurich), Thursday 15 April -- CHANGED TO BE ON ZOOM!!

[Chieh-Hsi Wu]

Dear all,

Hope everyone is having a good week!

Today at 2 PM, Prof Leonhard Held (University of Zurich) is giving an S3RI seminar on



"The assessment of replication success based on relative effect size."


Apologies for the short notice, due to unexpected IT issues, the seminar today (2 PM, UK time) is going to be on Zoom!!



Please see below for the Zoom details to join the seminar online and the details of the talk.



Hope to see you at the seminar later today!



Kind regards,

Jessie


<Zoom details for joining the seminar>

Topic: Southampton Seminar
Time: Apr 15, 2021 03:00 PM Zurich

Join Zoom Meeting
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Passcode: 430700

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Meeting ID: 672 2838 7078
Passcode: 430700



The assessment of replication success based on relative effect size
Leonard Held (University of Zurich)

Replication studies are increasingly conducted in order to confirm original findings. However, there is no established standard how to assess replication success and in practice many different approaches are used. The purpose of this paper is to refine and extend a recently proposed reverse-Bayes approach for the analysis of replication studies. We show how this method is directly related to the relative effect size, the ratio of the replication to the original effect estimate. This perspective leads to a new proposal to recalibrate the assessment of replication success, the golden level. The recalibration ensures that for borderline significant original studies replication success can only be achieved if the replication effect estimate is larger than the original one. Conditional power for replication success can then take any desired value if the original study is significant and the replication sample size is large enough. Compared to the standard approach to require statistical significance of both the original and replication study, replication success at the golden level offers uniform gains in project power and controls the Type-I error rate if the replication sample size is not smaller than the original one. An application to data from four large replication projects shows that the new approach leads to more appropriate inferences, as it penalizes shrinkage of the replication estimate compared to the original one, while ensuring that both effect estimates are sufficiently convincing on their own.

This is joint work with Charlotte Micheloud and Samuel Pawel


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