twoStageTMLEmsm: twoStageTMLEmsm

In twoStageDesignTMLE: Targeted Maximum Likelihood Estimation for Two-Stage Study Design

twoStageTMLEmsm

Description

Inverse probability of censoring weighted TMLE for evaluating MSM parameters when the full set of covariates is available on only a subset of observations, as in a 2-stage design.

Usage

twoStageTMLEmsm(
  Y,
  A,
  W,
  V,
  Delta.W,
  W.stage2,
  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", "V", "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 of interest (missingness allowed)
A	binary treatment indicator
W	matrix or data.frame of covariates measured on entire population
V	vector, matrix, or dataframe of covariates used to define MSM strata
Delta.W	Indicator of inclusion in subset with additional information
W.stage2	matrix or data.frame of covariates measured in subset population
Delta	binary indicator that outcome Y is observed
pi	optional vector of sampling probabilities
piform	optional parametric regression model for estimating pi
pi.SL.library	optional SL library specification for estimating pi (ignored when piform or pi is provided)
V.pi	optional number of cross-validation folds for super learning (ignored when piform or pi is provided)
pi.discreteSL	flag to indicate whether to use ensemble or discrete super learning (ignored when piform or pi is provided)
condSetNames	variables to condition on when estimating pi. Default is covariates in V and W.Can optionally also condition on A and/or Y.
id	optional indicator of independent units of observation
Q.family	outcome regression family, "gaussian" or "binomial"
augmentW	set to TRUE to augment W with predicted outcome values when A = 0 and A = 1
augW.SL.library	super learner library for preliminary outcome regression model (ignored when augmentW is FALSE)
rareOutcome	when TRUE sets V.Q = 20, Q.discreteSL = TRUE, Q.SL.library includes glm, glmnet, bart
verbose	when TRUE prints informative messages
...	other arugments passed to the tmleMSM function

Value

Object of class "twoStageTMLE"

tmle

Treatment effect estimates and summary information from call to tmleMSM function

twoStage

IPCW weight estimation summary, pi are the probabilities,coef are SL weights or coefficients from glm fit, type of estimation procedure, discreteSL flag indicating whether discrete super learning was used

augW

Matrix of predicted outcomes based on stage 1 covariates only

See Also

• tmle::tmleMSM() for details on customizing the estimation procedure

• twoStageTMLE() for estimating marginal effects

Examples

n <- 1000
set.seed(10)
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)
Y.bin <- rbinom(n, 1, plogis(-4.6 - 1.8* A + W1 + W2 -.3 *A*W1 + W3))
# Set 400 obs 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, misspecified outcome model (not recommended)
result1.MSM <- twoStageTMLEmsm(Y=Y, A=A, V= cbind(W1), W=cbind(W2), 
Delta.W = Delta.W, W.stage2 = W3.stage2, augmentW = FALSE,
piform = "Delta.W~ I(W1 > 0)", MSM = "A*W1", augW.SL.library = "SL.glm",
Qform = "Y~A+W1",gform="A~W1 + W2 +W3", hAVform = "A~1", verbose=TRUE)
summary(result1.MSM)

# 2. Call again, passing in previously estimated observation weights, 
# note that specifying a correct model for Q improves efficiency
result2.MSM <- twoStageTMLEmsm(Y=Y, A=A, V= cbind(W1), W=cbind(W2), 
Delta.W = Delta.W, W.stage2 = W3.stage2, augmentW = FALSE,
pi = result1.MSM$twoStage$pi, MSM = "A*W1",
Qform = "Y~ A + W1 + W2 + A*W1 + W3",gform="A~W1 + W2 +W3", hAVform = "A~1")
cbind(SE.Qmis = result1.MSM$tmle$se, SE.Qcor = result2.MSM$tmle$se)


#Binary outcome, augmentW, rareOutcome
result3.MSM <- twoStageTMLEmsm(Y=Y.bin, A=A, V= cbind(W1), W=cbind(W2), 
Delta.W = Delta.W, W.stage2 = W3.stage2, augmentW = TRUE,
piform = "Delta.W~ I(W1 > 0)", MSM = "A*W1", gform="A~W1 + W2 +W3",
 Q.family = "binomial", rareOutcome=TRUE)

twoStageDesignTMLE documentation built on Sept. 11, 2024, 6:38 p.m.