exp_biomarkertmle: TMLE procedure using ATE for Biomarker Identication from...
In biotmle: Targeted Learning with Moderated Statistics for Biomarker Discovery

This function performs influence curve-based estimation of the effect of an exposure on biological expression values associated with a given biomarker, controlling for a user-specified set of baseline covariates.

1	exp_biomarkertmle(Y, A, W, g_lib, Q_lib, cv_folds, ...)

`Y`	A `numeric` vector of expression values for a given biomarker.
`A`	A `numeric` vector of discretized exposure vector (e.g., from a design matrix whose effect on expression values is of interest.
`W`	A `Matrix` of `numeric` values corresponding to baseline covariates to be marginalized over in the estimation process.
`g_lib`	A `character` vector identifying the library of learning algorithms to be used in fitting the propensity score P[A = a \| W].
`Q_lib`	A `character` vector identifying the library of learning algorithms to be used in fitting the outcome regression E[Y \| A, W].
`cv_folds`	A `numeric` scalar indicating how many folds to use in performing targeted minimum loss estimation. Cross-validated estimates are more robust, allowing relaxing of theoretical conditions and construction of conservative variance estimates.
`...`	Additional arguments passed to `drtmle` in computing the targeted minimum loss estimator of the average treatment effect.

TMLE-based estimate of the relationship between biomarker expression and changes in an exposure variable, computed iteratively and saved in the tmleOut slot in a biotmle object.

biotmle documentation built on Nov. 8, 2020, 5:10 p.m.