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.
exp_biomarkertmle(Y, A, W, g_lib, Q_lib, cv_folds, ...)
Arguments
| 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. |
Value
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.