adaptest {adaptest} | R Documentation |
Computes marginal average treatment effects of a binary point treatment on multi-dimensional outcomes, adjusting for baseline covariates, using Targeted Minimum Loss-Based Estimation. A data-mining algorithm is used to perform biomarker selection before multiple testing to increase power.
adaptest(Y, A, W = NULL, n_top, n_fold, parameter_wrapper = rank_DE, learning_library = c("SL.glm", "SL.step", "SL.glm.interaction", "SL.gam", "SL.earth"), absolute = FALSE, negative = FALSE, p_cutoff = 0.05, q_cutoff = 0.05)
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 data-adaptive estimation algorithm |
n_fold |
(integer vector) - number of cross-validation folds. |
parameter_wrapper |
(function) - user-defined 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) - p-value cutoff (default as 0.05) at and below which to be considered significant. Used in inference stage. |
q_cutoff |
(numeric) - q-value cutoff (default as 0.05) at and below which to be considered significant. Used in multiple testing stage. |
S4 object of class data_adapt
, sub-classed from the container
class SummarizedExperiment
, with the following additional slots
containing data-mining selected biomarkers and their TMLE-based differential
expression and inference, as well as the original call to this function (for
user reference), respectively.
top_index
(integer vector) - indices for the data-mining
selected biomarkers
top_colname
(character vector) - names for the data-mining
selected biomarkers
top_colname_significant_q
(character vector) - names for the
data-mining 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) - p-values for the biomarkers in
top_colname
q_value
(numeric vector) - q-values 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 cross-validation folds for the biomarkers in top_colname
folds
(origami::folds class) - cross validation object
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)