biomarkertmle {biotmle} | R Documentation |
Computes the causal target parameter defined as the difference between the biomarker expression values under treatment and those same values under no treatment, using Targeted Minimum Loss-Based Estimation.
biomarkertmle(se, varInt, normalized = TRUE, ngscounts = FALSE, parallel = TRUE, bppar_type = NULL, future_param = NULL, family = "gaussian", subj_ids = NULL, g_lib = c("SL.mean", "SL.glm", "SL.earth"), Q_lib = c("SL.mean", "SL.glm", "SL.earth", "SL.ranger"), ...)
se |
(SummarizedExperiment) - containing expression or next-generation sequencing data in the "assays" slot and a matrix of phenotype-level data in the "colData" slot. |
varInt |
(numeric) - indicating the column of the design matrix
corresponding to the treatment or outcome of interest (in the
|
normalized |
(logical) - whether the data included in the |
ngscounts |
(logical) - whether the data are counts generated from a next-generation sequencing (NGS) experiment (e.g., RNA-seq). The default setting assumes continuous expression measures as generated by platforms that are microarray-type (i.e., so-called "targeted" assays). |
parallel |
(logical) - whether or not to use parallelization in the
estimation procedure. Invoking parallelization happens through a
combination of calls to |
bppar_type |
(character) - specifies the type of backend to be used with
the parallelization invoked by |
future_param |
(character) - specifies the type of parallelization to be
invoked when using futures for evaluation. For a list of the available
types, please consult the documentation for |
family |
(character) - specification of error family: "binomial" or "gaussian". |
subj_ids |
(numeric vector) - subject IDs to be passed directly to
subject should have the exact same numerical identifier; coerced to class
|
g_lib |
(char vector) - library of learning algorithms to be used in fitting the propensity score E[A | W] (the nuisance parameter denoted "g" in the literature on targeted minimum loss-based estimation). |
Q_lib |
(char vector) - library of learning algorithms to be used in fitting the outcome regression E[Y | A, W] (the nuisance parameter denoted "Q" in the literature on targeted minimum loss-based estimation). |
... |
Additional arguments to be passed directly to |
S4 object of class biotmle
, generated by sub-classing
SummarizedExperiment
, with additional slots containing tmleOut
and call
, among others, containing TMLE-based estimates of the
relationship between a biomarker and exposure or outcome variable and the
original call to this function (for user reference), respectively.
library(dplyr) library(biotmleData) data(illuminaData) library(SummarizedExperiment) "%ni%" <- Negate("%in%") colData(illuminaData) <- colData(illuminaData) %>% data.frame() %>% dplyr::mutate(age = as.numeric(age > median(age))) %>% DataFrame() varInt_index <- which(names(colData(illuminaData)) %in% "benzene") biomarkerTMLEout <- biomarkertmle( se = illuminaData[1:2, ], varInt = varInt_index, parallel = FALSE, family = "gaussian", g_lib = c("SL.mean", "SL.glm"), Q_lib = "SL.glm" ) #