Description
Usage
Arguments
Value
View source: R/biotmle.R
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.
| (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.
nhejazi/biotmle documentation built on Oct. 15, 2021, 5:46 p.m.
