# On optimal two-stage testing of multiple mediators

arXiv: https://arxiv.org/abs/2007.02844 · HTML: https://mlsanity.com/paper/2007.02844/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [A Reinforcement Learning Framework for Dynamic Mediation Analysis](https://mlsanity.com/paper/2301.13348/) (2023)

## Abstract (arXiv)

Mediation analysis in high-dimensional settings often involves identifying potential mediators among a large number of measured variables. For this purpose, a two-step familywise error rate procedure called ScreenMin has been recently proposed (Djordjilovi'c et al. 2019). In ScreenMin, variables are first screened and only those that pass the screening are tested. The proposed threshold for selection has been shown to guarantee asymptotic familywise error rate. In this work, we investigate the impact of the selection threshold on the finite sample familywise error rate. We derive a power maximizing selection threshold and show that it is well approximated by an adaptive threshold of Wang et al. (2016). We illustrate the investigated procedures on a case-control study examining the effect of fish intake on the risk of colorectal adenoma.