awot

Adaptively weighted joint test for main effect and genotype-by-treatment interaction effect for continuous endpoints.

A flexible and robust joint test of the single nucleotide polymorphism (SNP) main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures, Cauchy weighted joint test (CWOT) and adaptively weighted joint test (AWOT), are proposed to accurately calculate the joint test p-value. The proposed methods are evaluated through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in pharmacogenetics (PGx) association studies. For reference, see Hong Zhang, Devan Mehrotra and Judong Shen (2022) <doi:10.13140/RG.2.2.28323.53280>.

Hong Zhang

cwot

Cauchy Weighted Joint Test for Pharmacogenetics Analysis

awot function

<dl><dt>nullmod</dt>
<dd>- The null model object from the output of awot_null.</dd>
<dt>g</dt>
<dd>- The variable of interest, e.g., the genotype.</dd>
<dt>weights</dt>
<dd>- The pre-specified weights. The default choice is a vector of -1, -0.9,..., 0.9, 1.</dd></dl>

Arguments

Adaptively weighted joint test for main effect and genotype-by-treatment interaction effect for continuous endpoints. — awot

<dl>

<dt>nullmod</dt>
<dd>- The null model object from the output of awot_null.</dd>


<dt>g</dt>
<dd>- The variable of interest, e.g., the genotype.</dd>


<dt>weights</dt>
<dd>- The pre-specified weights. The default choice is a vector of -1, -0.9,..., 0.9, 1.</dd>

</dl>

awot: Adaptively weighted joint test for main effect and genotype-by-treatment interaction effect for continuous endpoints.

Description

Usage

Value

Arguments

References

Examples