Tail-area false discovery rates (FDRs) or 'q-values' are combined across multiple component tests to estimate a single q-value that represents the intersection of alternative hypotheses or the union of null hypotheses. In other words, this is an estimate of the rate at which tests called 'significant' at the specified omnibus q-value include at least one component condition that is called 'significant' due to chance alone.

 nonparametric

A likelihood-based hypothesis testing approach is implemented for
assessing causal mediation. Described in Millstein, Chen, and Breton (2016),
<DOI:10.1093/bioinformatics/btw135>, it could be used to test for mediation
of a known causal association between a DNA variant, the 'instrumental variable',
and a clinical outcome or phenotype by gene expression or DNA methylation, the
potential mediator. Another example would be testing mediation of the effect
of a drug on a clinical outcome by the molecular target. The hypothesis test
generates a p-value or permutation-based FDR value with confidence intervals
to quantify uncertainty in the causal inference. The outcome can be represented
by either a continuous or binary variable, the potential mediator is continuous,
and the instrumental variable can be continuous or binary and is not limited to
a single variable but may be a design matrix representing multiple variables.

Joshua Millstein

Causal Inference Test

iuq function

<dl> <dt>qvec</dt>
<dd>vector of q-values, each corresponding to a component test.</dd></dl>

Arguments

Author

Intersection/Union Q-Value — iuq

<dl>

 <dt>qvec</dt>
<dd>vector of q-values, each corresponding to a component test.</dd>

</dl>

iuq: Intersection/Union Q-Value

Description

Usage

Value

Arguments

Author

Details

References

Examples