hima_quantile: High-dimensional quantile mediation analysis

Description

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

Usage

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

Value

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

Index:: mediation name of selected significant mediator.
alpha_hat:: coefficient estimates of exposure (X) --> mediators (M) (adjusted for covariates).
alpha_se:: standard error for alpha.
beta_hat:: coefficient estimates of mediators (M) --> outcome (Y) (adjusted for covariates and exposure).
beta_se:: standard error for beta.
IDE:: mediation (indirect) effect, i.e., alpha*beta.
rimp:: relative importance of the mediator.
pmax:: joint raw p-value of selected significant mediator (based on Bonferroni method).

Arguments

X: a vector of exposure. Do not use data.frame or matrix.
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 select significant mediators. Default = 0.05.
verbose: logical. Should the function be verbose? Default = FALSE.
...: reserved passing parameter.

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

Run this code

if (FALSE) {
# Note: In the following example, M1, M2, and M3 are true mediators.

head(QuantileData$PhenoData)

hima_quantile.fit <- hima_quantile(
  X = QuantileData$PhenoData$Treatment,
  M = QuantileData$Mediator,
  Y = QuantileData$PhenoData$Outcome,
  COV = QuantileData$PhenoData[, c("Sex", "Age")],
  tau = c(0.3, 0.5, 0.7),
  scale = FALSE, # Disabled only for simulation data
  Bonfcut = 0.05,
  verbose = TRUE
)
hima_quantile.fit
}