biomarkerTMLE_exposure {biotmle} | R Documentation |
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.
biomarkerTMLE_exposure(Y, W, A, a, subj_ids = NULL, family = "gaussian", g_lib, Q_lib, ...)
Y |
A |
W |
A |
A |
A |
a |
The |
subj_ids |
A |
family |
(character) - specification of error family: "binomial" or "gaussian" |
g_lib |
(char vector) - library of learning algorithms to be used in fitting the propensity score E[A | W] (the nuisance parameter denoted "g" in the literature on targeted minimum loss-based estimation). |
Q_lib |
(char vector) - library of learning algorithms to be used in fitting the outcome regression E[Y | A, W] (the nuisance parameter denoted "Q" in the literature on targeted minimum loss-based estimation). |
... |
Additional arguments to be passed directly to |
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.