twoStageTMLE {twoStageDesignTMLE} | R Documentation |
twoStageTMLE
Description
Inverse probability of censoring weighted TMLE for evaluating parameters when the full set of covariates is available on only a subset of observations.
Usage
twoStageTMLE(
Y,
A,
W,
Delta.W,
W.stage2,
Z = NULL,
Delta = rep(1, length(Y)),
pi = NULL,
piform = NULL,
pi.SL.library = c("SL.glm", "SL.gam", "SL.glmnet", "tmle.SL.dbarts.k.5"),
V.pi = 10,
pi.discreteSL = TRUE,
condSetNames = c("A", "W", "Y"),
id = NULL,
Q.family = "gaussian",
augmentW = TRUE,
augW.SL.library = c("SL.glm", "SL.glmnet", "tmle.SL.dbarts2"),
rareOutcome = FALSE,
verbose = FALSE,
...
)
Arguments
Y |
outcome |
A |
binary treatment indicator |
W |
covariate matrix observed on everyone |
Delta.W |
binary indicator of missing second stage covariates |
W.stage2 |
matrix of second stage covariates observed on subset of observations |
Z |
optional mediator of treatment effect for evaluating a controlled direct effect |
Delta |
binary indicator of missing value for outcome |
pi |
optional vector of missingness probabilities for |
piform |
parametric regression formula for estimating |
pi.SL.library |
super learner library for estimating |
V.pi |
number of cross validation folds for estimating |
pi.discreteSL |
Use discrete super learning when |
condSetNames |
Variables to include as predictors of missingness
in |
id |
Identifier of independent units of observation, e.g., clusters |
Q.family |
Regression family for the outcome |
augmentW |
When |
augW.SL.library |
super learner library for preliminary outcome
regression model (ignored when |
rareOutcome |
When |
verbose |
When |
... |
other parameters passed to the tmle function (not checked) |
Details
When using piform
to specify a parametric model for pi that conditions
on the outcome use Delta.W
as the dependent variable and Y.orig
on the right hand side of the formula instead of Y
. When writing a
user-defined SL wrapper for inclusion in pi.SL.library
use Y
on the left hand side of the formula. If specific covariate names are
used on the right hand side use Y.orig
to condition
on the outcome.
Value
object of class 'twoStageTMLE'.
tmle |
Treatment effect estimates and summary information |
twoStage |
IPCW weight estimation summary, |
augW |
Matrix of predicted outcomes based on stage 1 covariates only |
See Also
-
tmle::tmle()
for details on customizing the estimation procedure -
twoStageTMLEmsm()
for estimating conditional effects S Rose and MJ van der Laan. A Targeted Maximum Likelihood Estimator for Two-Stage Designs. Int J Biostat. 2011 Jan 1; 7(1): 17. doi:10.2202/1557-4679.1217
Examples
n <- 1000
W1 <- rnorm(n)
W2 <- rnorm(n)
W3 <- rnorm(n)
A <- rbinom(n, 1, plogis(-1 + .2*W1 + .3*W2 + .1*W3))
Y <- 10 + A + W1 + W2 + A*W1 + W3 + rnorm(n)
d <- data.frame(Y, A, W1, W2, W3)
# Set 400 with data on W3, more likely if W1 > 1
n.sample <- 400
p.sample <- 0.5 + .2*(W1 > 1)
rows.sample <- sample(1:n, size = n.sample, p = p.sample)
Delta.W <- rep(0,n)
Delta.W[rows.sample] <- 1
W3.stage2 <- cbind(W3 = W3[Delta.W==1])
#1. specify parametric models and do not augment W (fast, but not recommended)
result1 <- twoStageTMLE(Y=Y, A=A, W=cbind(W1, W2), Delta.W = Delta.W,
W.stage2 = W3.stage2, piform = "Delta.W~ I(W1 > 0) + Y.orig", V.pi= 5,
verbose = TRUE, Qform = "Y~A+W1",gform="A~W1 + W2 +W3", augmentW = FALSE)
summary(result1)
#2. specify a parametric model for conditional missingness probabilities (pi)
# and use default values to estimate marginal effect using \code{tmle}
result2 <- twoStageTMLE(Y=Y, A=A, W=cbind(W1, W2), Delta.W = Delta.W,
W.stage2 = cbind(W3)[Delta.W == 1], piform = "Delta.W~ I(W1 > 0)",
V.pi= 5,verbose = TRUE)
result2