Description Usage Arguments Value Examples
Computes marginal average treatment effects of a binary point treatment on multidimensional outcomes, adjusting for baseline covariates, using Targeted Minimum LossBased Estimation. A datamining algorithm is used to perform biomarker selection before multiple testing to increase power.
1 2 3 4 
Y 
(numeric vector)  A 
A 
(numeric vector)  binary treatment indicator:

W 
(numeric vector, numeric matrix, or numeric data.frame)  matrix of baseline covariates where each column correspond to one baseline covariate and each row corresponds to one observation. 
n_top 
(integer vector)  value for the number of candidate covariates to generate using the dataadaptive estimation algorithm 
n_fold 
(integer vector)  number of crossvalidation folds. 
parameter_wrapper 
(function)  userdefined function that takes input
(Y, A, W, absolute, negative) and outputs a (integer vector) containing
ranks of biomarkers (outcome variables). For details, please refer to the
documentation for 
learning_library 
(character vector)  library of learning algorithms to be used in fitting the "Q" and "g" step of the standard TMLE procedure. 
absolute 
(logical)  whether or not to test for absolute effect size.
If 
negative 
(logical)  whether or not to test for negative effect size.
If 
p_cutoff 
(numeric)  pvalue cutoff (default as 0.05) at and below which to be considered significant. Used in inference stage. 
q_cutoff 
(numeric)  qvalue cutoff (default as 0.05) at and below which to be considered significant. Used in multiple testing stage. 
S4 object of class data_adapt
, subclassed from the container
class SummarizedExperiment
, with the following additional slots
containing datamining selected biomarkers and their TMLEbased differential
expression and inference, as well as the original call to this function (for
user reference), respectively.
top_index
(integer vector)  indices for the datamining
selected biomarkers
top_colname
(character vector)  names for the datamining
selected biomarkers
top_colname_significant_q
(character vector)  names for the
datamining selected biomarkers, which are significant after multiple
testing stage
DE
(numeric vector)  differential expression effect sizes for
the biomarkers in top_colname
p_value
(numeric vector)  pvalues for the biomarkers in
top_colname
q_value
(numeric vector)  qvalues for the biomarkers in
top_colname
significant_q
(integer vector)  indices of top_colname
which is significant after multiple testing stage.
mean_rank_top
(numeric vector)  average ranking across folds
of crossvalidation folds for the biomarkers in top_colname
folds
(origami::folds class)  cross validation object
1 2 3 4 5 6 7 8 9 10 11 12 13 14  set.seed(1234)
data(simpleArray)
simulated_array < simulated_array
simulated_treatment < simulated_treatment
adaptest(Y = simulated_array,
A = simulated_treatment,
W = NULL,
n_top = 5,
n_fold = 3,
learning_library = 'SL.glm',
parameter_wrapper = adaptest::rank_DE,
absolute = FALSE,
negative = FALSE)

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