| Title: | Cauchy Weighted Joint Test for Pharmacogenetics Analysis |
|---|---|
| Description: | 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>. |
| Authors: | Hong Zhang [aut, cre] |
| Maintainer: | Hong Zhang <[email protected]> |
| License: | GPL-2 |
| Version: | 0.1.0 |
| Built: | 2024-12-25 04:41:24 UTC |
| Source: | https://github.com/cran/cwot |
Adaptively weighted joint test for main effect and genotype-by-treatment interaction effect for continuous endpoints.
awot(nullmod, g, weights = seq(-1, 1, 0.1))awot(nullmod, g, weights = seq(-1, 1, 0.1))
nullmod |
- The null model object from the output of awot_null. |
g |
- The variable of interest, e.g., the genotype. |
weights |
- The pre-specified weights. The default choice is a vector of -1, -0.9,..., 0.9, 1. |
The p-value of AWOT and the individual p-values of the composite genotypes.
Hong Zhang, Qing Li, Devan Mehrotra and Judong Shen. "CauchyCP: a powerful test under non-proportional hazards using Cauchy combination of change-point Cox regressions", arXiv:2101.00059.
n = 100 y = rbinom(n, 1, 0.3) x = data.frame(x1=rnorm(n)) tr = rbinom(n, 1, 0.5) g = rbinom(n, 2, 0.1) nullmod = awot_null(y, tr, x) awot(nullmod, g, weights=seq(-1,1,0.1))n = 100 y = rbinom(n, 1, 0.3) x = data.frame(x1=rnorm(n)) tr = rbinom(n, 1, 0.5) g = rbinom(n, 2, 0.1) nullmod = awot_null(y, tr, x) awot(nullmod, g, weights=seq(-1,1,0.1))
Prepare null model for awot.
awot_null(y, tr, x = NULL)awot_null(y, tr, x = NULL)
y |
- Continuous response variable. |
tr |
- Binary treatment variable, 1 for treated, 0 for placebo. |
x |
- Covariates in addition to treatment variable. |
A list of objects needed for awot.
Hong Zhang, Devan Mehrotra and Judong Shen, "AWOT and CWOT for Genotype and Genotype by Treatment Interaction Joint Analysis in Pharmacogenetics GWAS".
n = 100 y = rnorm(n) x = data.frame(x1=rnorm(n)) tr = rbinom(n, 1, 0.5) nullmod = awot_null(y, tr, x)n = 100 y = rnorm(n) x = data.frame(x1=rnorm(n)) tr = rbinom(n, 1, 0.5) nullmod = awot_null(y, tr, x)
Cauchy weighted joint test for main effect and genotype-by-treatment interaction effect for binary endpoints.
cwot(nullmod, g, weights = seq(-1, 1, 0.1))cwot(nullmod, g, weights = seq(-1, 1, 0.1))
nullmod |
- The null model object from the output of cwot_null. |
g |
- The variable of interest, e.g., the genotype. |
weights |
- The pre-specified weights. The default choice is a vector of -1, -0.9,..., 0.9, 1. |
The p-values of CWOT, CWOT_Score, CWOT_LRT and the individual p-values of the composite genotypes.
Hong Zhang, Qing Li, Devan Mehrotra and Judong Shen. "CauchyCP: a powerful test under non-proportional hazards using Cauchy combination of change-point Cox regressions", arXiv:2101.00059.
n = 100 y = rbinom(n, 1, 0.3) x = data.frame(x1=rnorm(n)) tr = rbinom(n, 1, 0.5) g = rbinom(n, 2, 0.1) nullmod = cwot_null(y, tr, x) cwot(nullmod, g, weights=seq(-1,1,0.1))n = 100 y = rbinom(n, 1, 0.3) x = data.frame(x1=rnorm(n)) tr = rbinom(n, 1, 0.5) g = rbinom(n, 2, 0.1) nullmod = cwot_null(y, tr, x) cwot(nullmod, g, weights=seq(-1,1,0.1))
Prepare null model for cwot.
cwot_null(y, tr, x = NULL)cwot_null(y, tr, x = NULL)
y |
- Binary response variable. |
tr |
- Binary treatment variable, 1 for treated, 0 for placebo. |
x |
- Covariates in addition to treatment variable. |
A list of objects needed for cwot.
Hong Zhang, Devan Mehrotra and Judong Shen, "AWOT and CWOT for Genotype and Genotype by Treatment Interaction Joint Analysis in Pharmacogenetics GWAS".
n = 100 y = rbinom(n, 1, 0.3) x = data.frame(x1=rnorm(n)) tr = rbinom(n, 1, 0.5) nullmod = cwot_null(y, tr, x)n = 100 y = rbinom(n, 1, 0.3) x = data.frame(x1=rnorm(n)) tr = rbinom(n, 1, 0.5) nullmod = cwot_null(y, tr, x)