Biomarker Evaluation with Targeted Minimum Loss Estimation of the ATE

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 Estimation.

biomarkertmle(
  se,
  varInt,
  normalized = TRUE,
  ngscounts = FALSE,
  bppar_type = BiocParallel::MulticoreParam(),
  bppar_debug = FALSE,
  cv_folds = 1,
  g_lib = c("SL.mean", "SL.glm", "SL.bayesglm"),
  Q_lib = c("SL.mean", "SL.bayesglm", "SL.earth", "SL.ranger"),
  ...
)

Arguments

se	A `SummarizedExperiment` containing microarray expression or next-generation sequencing data in the `assays` slot and a matrix of phenotype-level data in the `colData` slot.
varInt	A `numeric` indicating the column of the design matrix corresponding to the treatment or outcome of interest (in the `colData` slot of the `SummarizedExperiment` argument "se").
normalized	A `logical` indicating whether the data included in the `assay` slot of the input `SummarizedExperiment` object has been normalized externally. The default is set to `TRUE` with the expectation that an appropriate normalization method has been applied. If set to `FALSE`, median normalization is performed for microarray data.
ngscounts	A `logical` indicating whether the data are counts generated from a next-generation sequencing experiment (e.g., RNA-seq). The default setting assumes continuous expression measures as generated by microarray platforms.
bppar_type	A parallelization option specified by `BiocParallel`. Consult the manual page for `BiocParallelParam` for possible types and their descriptions. The default for this argument is `MulticoreParam`, for multicore evaluation.
bppar_debug	A `logical` indicating whether or not to rely upon pkgBiocParallel. Setting this argument to `TRUE`, replaces the call to `bplapply` by a call to `lapply`, which significantly reduces the overhead of debugging. Note that invoking this option overrides all other parallelization arguments.
cv_folds	A `numeric` scalar indicating how many folds to use in performing targeted minimum loss estimation. Cross-validated estimates have been demonstrated to allow relaxation of certain theoretical conditions and and accommodate the construction of more conservative variance estimates.
g_lib	A `character` vector specifying the library of machine learning algorithms for use in fitting the propensity score P(A = a \| W).
Q_lib	A `character` vector specifying the library of machine learning algorithms for use in fitting the outcome regression E[Y \| A,W].
...	Additional arguments to be passed to `drtmle` in computing the targeted minimum loss estimator of the average treatment effect.

Value

S4 object of class biotmle, inheriting from SummarizedExperiment, with additional slots tmleOut and call, among others, containing TML estimates of the ATE of exposure on biomarker expression.

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:Biobase’:
#> 
#>     combine
#> The following objects are masked from ‘package:GenomicRanges’:
#> 
#>     intersect, setdiff, union
#> The following object is masked from ‘package:GenomeInfoDb’:
#> 
#>     intersect
#> The following objects are masked from ‘package:IRanges’:
#> 
#>     collapse, desc, intersect, setdiff, slice, union
#> The following objects are masked from ‘package:S4Vectors’:
#> 
#>     first, intersect, rename, setdiff, setequal, union
#> The following objects are masked from ‘package:BiocGenerics’:
#> 
#>     combine, intersect, setdiff, union
#> The following object is masked from ‘package:matrixStats’:
#> 
#>     count
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(biotmleData)
library(SuperLearner)
#> Loading required package: nnls
#> Loading required package: gam
#> Loading required package: splines
#> Loading required package: foreach
#> Loaded gam 1.20
#> Super Learner
#> Version: 2.0-28
#> Package created on 2021-05-04
library(SummarizedExperiment)
data(illuminaData)

colData(illuminaData) <- colData(illuminaData) %>%
  data.frame() %>%
  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")
)
#> 
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#> Loading required package: quadprog
#> Loading required package: nloptr
#> 
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#>