HIMA (version 2.3.3)

hima_survival: High-dimensional mediation analysis for survival outcome data

Description

hima_survival is used to estimate and test high-dimensional mediation effects for survival data.

Usage

hima_survival(
  X,
  M,
  OT,
  status,
  COV = NULL,
  topN = NULL,
  scale = TRUE,
  FDRcut = 0.05,
  verbose = FALSE,
  parallel = FALSE,
  ncore = 1
)

Value

A data.frame containing mediation testing results of significant mediators (FDR <FDRcut).

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 HDMT pointwise FDR 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.

OT

a vector of observed failure times.

status

a vector of censoring indicator (status = 1: uncensored; status = 0: censored)

COV

a matrix of adjusting covariates. Rows represent samples, columns represent variables. Can be NULL.

topN

an integer specifying the number of top markers from sure independent screening. Default = NULL. If NULL, topN will be 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).

scale

logical. Should the function scale the data? Default = TRUE.

FDRcut

HDMT pointwise FDR cutoff applied to select significant mediators. Default = 0.05.

verbose

logical. Should the function be verbose? Default = FALSE.

parallel

logical. Enable parallel computing feature? Default = FALSE.

ncore

number of cores to run parallel computing Valid when parallel = TRUE.

References

Zhang H, Zheng Y, Hou L, Zheng C, Liu L. Mediation Analysis for Survival Data with High-Dimensional Mediators. Bioinformatics. 2021. DOI: 10.1093/bioinformatics/btab564. PMID: 34343267; PMCID: PMC8570823

Examples

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

data(SurvivalData)
pheno_data <- SurvivalData$PhenoData
mediator_data <- SurvivalData$Mediator

hima_survival.fit <- hima_survival(
  X = pheno_data$Treatment,
  OT = pheno_data$Time,
  status = pheno_data$Status,
  M = mediator_data,
  COV = pheno_data[, c("Sex", "Age")],
  scale = FALSE, # Disabled only for simulation data
  FDRcut = 0.05,
  verbose = TRUE
)
hima_survival.fit
}