Add like
Add dislike
Add to saved papers

Gaining power in multiple testing of interval hypotheses via conditionalization.

Biostatistics 2018 September 22
In this article, we introduce a novel procedure for improving power of multiple testing procedures (MTPs) of interval hypotheses. When testing interval hypotheses the null hypothesis $P$-values tend to be stochastically larger than standard uniform if the true parameter is in the interior of the null hypothesis. The new procedure starts with a set of $P$-values and discards those with values above a certain pre-selected threshold, while the rest are corrected (scaled-up) by the value of the threshold. Subsequently, a chosen family-wise error rate (FWER) or false discovery rate MTP is applied to the set of corrected $P$-values only. We prove the general validity of this procedure under independence of $P$-values, and for the special case of the Bonferroni method, we formulate several sufficient conditions for the control of the FWER. It is demonstrated that this "filtering" of $P$-values can yield considerable gains of power.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app