Estimate TE for BKMR-CMA-MI

Get Ya and Yastar used in effects caculation

Get Ya and Yastar used in effects caculation

YaYastar.SamplePred(a, astar, e.y, fit.y.TE, X.predict.Y, sel, seed)

YaYastar.SamplePred(a, astar, e.y, fit.y.TE, X.predict.Y, sel, seed)

Arguments

a

exposure variables at current level

astar

exposure variables at counterfactual level

e.y

effect modifier for the outcome variable

fit.y.TE

total effect model fit regressing outcome on exposures, effect modifiers and confounders on outcome

X.predict.Y

counfounders for outcome

sel

a vector selecting which iterations of the fit should be retained or inference

seed

the random seed to use to evaluate the code

BKMRfits.y.TE

A list of BKMR models for total effect model fit regressing outcome on exposures, effect modifiers and confounders on outcome, using multiple imputed data.

Value

A list containing the sample prediction for Ya and Yastar

A list containing the sample prediction for Ya and Yastar

Details

For guided examples, go to https://zc2326.github.io/causalbkmr/articles/BKMRCMA_QuickStart.html

For guided examples, go to https://zc2326.github.io/causalbkmr/articles/BKMRCMA_QuickStart.html

Examples

if (FALSE) {
library(causalbkmr)
dat <-  cma_sampledata(N=300, L=3, P=3, scenario=1, seed=7)

A <- cbind(dat$z1, dat$z2, dat$z3)
X <- cbind(dat$x3)
y  <- dat$y
m  <- dat$M

E.M <- NULL
E.Y <- dat$x2

Z.M <- cbind(A,E.M)
Z.Y <- cbind(A, E.Y)
Zm.Y <- cbind(Z.Y, m)

set.seed(1)
fit.y <- kmbayes(y=y, Z=Zm.Y, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.y,file="bkmr_y.RData")

set.seed(2)
fit.y.TE <- kmbayes(y=y, Z=Z.Y, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.y.TE,file="bkmr_y_TE.RData")

set.seed(3)
fit.m <- kmbayes(y=m, Z=Z.M, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.m,file="bkmr_m.RData")

X.predict <- matrix(colMeans(X),nrow=1)
astar <- c(apply(A, 2, quantile, probs=0.25))
a <- c(apply(A, 2, quantile, probs=0.75))

e.y10 = quantile(E.Y, probs=0.1)
e.y90 = quantile(E.Y, probs=0.9)

sel<-seq(2500,5000,by=5)

TE.ey10 <- TE.bkmr(a=a, astar=astar, e.y = e.y10, fit.y.TE=fit.y.TE, X.predict=X.predict, alpha=0.05, sel=sel, seed=122)
TE.ey90 <- TE.bkmr(a=a, astar=astar, e.y = e.y90, fit.y.TE=fit.y.TE, X.predict=X.predict, alpha=0.05, sel=sel, seed=122)
TE.ey10$est
TE.ey90$est
}

if (FALSE) {
library(causalbkmr)
dat <-  cma_sampledata(N=300, L=3, P=3, scenario=1, seed=7)

A <- cbind(dat$z1, dat$z2, dat$z3)
X <- cbind(dat$x3)
y  <- dat$y
m  <- dat$M

E.M <- NULL
E.Y <- dat$x2

Z.M <- cbind(A,E.M)
Z.Y <- cbind(A, E.Y)
Zm.Y <- cbind(Z.Y, m)

set.seed(1)
fit.y <- kmbayes(y=y, Z=Zm.Y, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.y,file="bkmr_y.RData")

set.seed(2)
fit.y.TE <- kmbayes(y=y, Z=Z.Y, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.y.TE,file="bkmr_y_TE.RData")

set.seed(3)
fit.m <- kmbayes(y=m, Z=Z.M, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.m,file="bkmr_m.RData")

X.predict <- matrix(colMeans(X),nrow=1)
astar <- c(apply(A, 2, quantile, probs=0.25))
a <- c(apply(A, 2, quantile, probs=0.75))

e.y10 = quantile(E.Y, probs=0.1)
e.y90 = quantile(E.Y, probs=0.9)

sel<-seq(2500,5000,by=5)

TE.ey10 <- TE.bkmr(a=a, astar=astar, e.y = e.y10, fit.y.TE=fit.y.TE, X.predict=X.predict, alpha=0.05, sel=sel, seed=122)
TE.ey90 <- TE.bkmr(a=a, astar=astar, e.y = e.y90, fit.y.TE=fit.y.TE, X.predict=X.predict, alpha=0.05, sel=sel, seed=122)
TE.ey10$est
TE.ey90$est
}