adaptest {adaptest} | R Documentation

## Data-adaptive Statistics for High-Dimensional Multiple Testing

### Description

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.

### Usage

    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)

### Arguments

`Y` | (numeric vector) - A `data.frame` or `matrix` of binary or continuous biomarker measures (outcome variables). Alternatively, this will be an object of class `adapTMLE` if the wrapper `bioadaptest` is invoked (n.b., the wrapper is the preferred interface for standard data analytic use-cases arising in computational and genomic biology).
`A` | (numeric vector) - binary treatment indicator: `1` = treatment, `0` = control
`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 `rank_DE`
`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 `FALSE`, test for directional effect. This overrides argument `negative`.
`negative` | (logical) - whether or not to test for negative effect size. If `FALSE` = test for positive effect size. This is effective only when `absolute = FALSE`.
`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.

### Value

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

### Examples

    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)

[Package adaptest version 1.2.0 Index]