survHIMA {HIMA} | R Documentation |
High-dimensional mediation analysis for survival data
Description
survHIMA
is used to estimate and test high-dimensional mediation effects for survival data.
Usage
survHIMA(X, Z, M, OT, status, FDRcut = 0.05, scale = TRUE, verbose = FALSE)
Arguments
X |
a vector of exposure. |
Z |
a matrix of adjusting covariates. Rows represent samples, columns represent variables. Can be |
M |
a |
OT |
a vector of observed failure times. |
status |
a vector of censoring indicator ( |
FDRcut |
FDR cutoff applied to define and select significant mediators. Default = |
scale |
logical. Should the function scale the data? Default = |
verbose |
logical. Should the function be verbose? Default = |
Value
A data.frame containing mediation testing results of selected mediators (FDR <FDPcut
).
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.
p.joint: joint raw p-value of selected significant mediator (based on FDR).
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
## Not run:
# Note: In the following example, M1, M2, and M3 are true mediators.
data(himaDat)
head(himaDat$Example3$PhenoData)
survHIMA.fit <- survHIMA(X = himaDat$Example3$PhenoData$Treatment,
Z = himaDat$Example3$PhenoData[, c("Sex", "Age")],
M = himaDat$Example3$Mediator,
OT = himaDat$Example3$PhenoData$Time,
status = himaDat$Example3$PhenoData$Status,
FDRcut = 0.05,
scale = FALSE,
verbose = TRUE)
survHIMA.fit
## End(Not run)