biomarkerTMLE_exposure {biotmle}R Documentation

TMLE procedure for Biomarker Identication from Exposure

Description

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

Usage

biomarkerTMLE_exposure(Y, W, A, a, subj_ids = NULL, family = "gaussian",
  g_lib, Q_lib)

Arguments

Y

(numeric vector) - a vector of expression values for a single biomarker.

W

(numeric matrix) - a matrix of baseline covariates to be controlled in the estimation process.

A

(numeric vector) - a discretized exposure vector (e.g., from a design matrix whose effect on expression values is of interest.

a

(numeric vector) - the levels of A against which comparisons are to be made.

subj_ids

(numeric vector) - subject IDs to be passed directly to the same subject should have the exact same numerical identifier. coerced to numeric if not provided in the appropriate form.

family

(character) - specification of error family: "binomial" or "gaussian"

g_lib

(char vector) - library of learning algorithms to be used in fitting the "g" step of the standard TMLE procedure.

Q_lib

(char vector) - library of learning algorithms to be used in fitting the "Q" step of the standard TMLE procedure.

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.


[Package biotmle version 1.4.0 Index]