Performs variance shrinkage via application of an empirical Bayes procedure (of LIMMA) on the observed data after a transformation moving the data to influence function space, based on the average treatment effect parameter.

modtest_ic(biotmle, adjust = "BH", pval_type = c("normal", "logistic"), ...)



biotmle object as generated by biomarkertmle


the multiple testing correction to be applied to p-values that are generated from the moderated tests. The recommended (default) method is that of Benjamini and Hochberg. See topTable for a list of appropriate methods.


The reference distribution to be used for computing the p-value. Those based on the normal approximation tend to provide misleading inference when working with moderately sized (finite) samples. Use of the logistic distribution has been found to empirically improve performance in settings where multiple hypothesis testing is a concern.


Other arguments passed to topTable.


biotmle object containing the results of applying both lmFit and topTable.


library(dplyr) library(biotmleData) library(SuperLearner) library(SummarizedExperiment) data(illuminaData) colData(illuminaData) <- colData(illuminaData) %>% data.frame() %>% dplyr::mutate(age = as.numeric(age > median(age))) %>% DataFrame() benz_idx <- which(names(colData(illuminaData)) %in% "benzene") biomarkerTMLEout <- biomarkertmle( se = illuminaData[1:2, ], varInt = benz_idx, bppar_type = BiocParallel::SerialParam(), g_lib = c("SL.mean", "SL.glm"), Q_lib = c("SL.mean", "SL.glm") )
#> | | | 0% | |=================================== | 50% | |======================================================================| 100% #>
limmaTMLEout <- modtest_ic(biotmle = biomarkerTMLEout)