qHIMA: High-dimensional quantile mediation analysis

In YinanZheng/HMA: High-Dimensional Mediation Analysis

High-dimensional quantile mediation analysis

Description

qHIMA is used to estimate and test high-dimensional quantile mediation effects.

Usage

qHIMA(
  X,
  M,
  Y,
  COV = NULL,
  penalty = c("MCP", "SCAD", "lasso"),
  topN = NULL,
  tau = 0.5,
  scale = TRUE,
  Bonfcut = 0.05,
  verbose = FALSE,
  ...
)

Arguments

X	a vector of exposure.
M	a data.frame or matrix of high-dimensional mediators. Rows represent samples, columns represent mediator variables.
Y	a vector of continuous outcome. Do not use data.frame or matrix.
COV	a matrix of adjusting covariates. Rows represent samples, columns represent variables. Can be NULL.
penalty	the penalty to be applied to the model (a parameter passed to function conquer.cv.reg in package conquer. Either 'MCP' (the default), 'SCAD', or 'lasso'.
topN	an integer specifying the number of top markers from sure independent screening. Default = NULL. If NULL, topN will be 2*ceiling(n/log(n)), where n is the sample size. If the sample size is greater than topN (pre-specified or calculated), all mediators will be included in the test (i.e. low-dimensional scenario).
tau	quantile level of outcome. Default = 0.5. A vector of tau is accepted.
scale	logical. Should the function scale the data? Default = TRUE.
Bonfcut	Bonferroni-corrected p value cutoff applied to define and select significant mediators. Default = 0.05.
verbose	logical. Should the function be verbose? Default = FALSE.
...	other arguments.

Value

A data.frame containing mediation testing results of selected mediators (Bonferroni-adjusted p value <Bonfcut).

• ID: index of selected significant mediator.

• alpha: coefficient estimates of exposure (X) –> mediators (M).

• alpha_se: standard error for alpha.

• beta: coefficient estimates of mediators (M) –> outcome (Y) (adjusted for exposure).

• beta_se: standard error for beta.

• Bonferroni.p: statistical significance of the mediator (Bonferroni-corrected p value).

References

Zhang H, Hong X, Zheng Y, Hou L, Zheng C, Wang X, Liu L. High-Dimensional Quantile Mediation Analysis with Application to a Birth Cohort Study of Mother–Newborn Pairs. Bioinformatics. 2024. DOI: 10.1093/bioinformatics/btae055. PMID: 38290773; PMCID: PMC10873903

Examples

## Not run: 
# Note: In the following example, M1, M2, and M3 are true mediators.
data(himaDat)

head(himaDat$Example5$PhenoData)

qHIMA.fit <- qHIMA(X = himaDat$Example5$PhenoData$Treatment,
                M = himaDat$Example5$Mediator, 
                Y = himaDat$Example5$PhenoData$Outcome, 
                COV = himaDat$Example5$PhenoData[, c("Sex", "Age")], 
                Bonfcut = 0.05,
                tau = c(0.3, 0.5, 0.7),
                scale = FALSE, 
                verbose = TRUE)
qHIMA.fit

## End(Not run)

YinanZheng/HMA documentation built on April 23, 2024, 4:55 a.m.