summary.riskCurve {pssmooth} | R Documentation |
Summary of Point and Interval Estimation of a Marginal Causal Effect Predictiveness Curve
Description
Summarizes point estimates and pointwise and simultaneous Wald-type bootstrap confidence intervals for a specified marginal causal effect predictiveness (mCEP) curve (see, e.g., Juraska, Huang, and Gilbert (2018) for the definition).
Usage
## S3 method for class 'riskCurve'
summary(
object,
boot = NULL,
contrast = c("te", "rr", "logrr", "rd"),
confLevel = 0.95,
...
)
Arguments
object |
an object of class |
boot |
an object returned by |
contrast |
a character string specifying the mCEP curve. It must be one of |
confLevel |
the confidence level of pointwise and simultaneous confidence intervals |
... |
for other methods |
Value
A data frame containing point and possibly interval estimates of the specified mCEP curve.
References
Juraska, M., Huang, Y., and Gilbert, P. B. (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560, https://doi.org/10.1093/biostatistics/kxy074.
See Also
Examples
n <- 500
Z <- rep(0:1, each=n/2)
S <- MASS::mvrnorm(n, mu=c(2,2,3), Sigma=matrix(c(1,0.9,0.7,0.9,1,0.7,0.7,0.7,1), nrow=3))
p <- pnorm(drop(cbind(1,Z,(1-Z)*S[,2],Z*S[,3]) %*% c(-1.2,0.2,-0.02,-0.2)))
Y <- sapply(p, function(risk){ rbinom(1,1,risk) })
# delete S(1) in placebo recipients
S[Z==0,3] <- NA
# delete S(0) in treatment recipients
S[Z==1,2] <- NA
# generate the indicator of being sampled into the phase 2 subset
phase2 <- rbinom(n,1,0.4)
# delete Sb, S(0) and S(1) in controls not included in the phase 2 subset
S[Y==0 & phase2==0,] <- c(NA,NA,NA)
# delete Sb in cases not included in the phase 2 subset
S[Y==1 & phase2==0,1] <- NA
data <- data.frame(Z,S[,1],ifelse(Z==0,S[,2],S[,3]),Y)
colnames(data) <- c("Z","Sb","S","Y")
qS <- quantile(data$S, probs=c(0.05,0.95), na.rm=TRUE)
grid <- seq(qS[1], qS[2], length.out=2)
out <- riskCurve(formula=Y ~ S, bsm="Sb", tx="Z", data=data, psGrid=grid)
boot <- bootRiskCurve(formula=Y ~ S, bsm="Sb", tx="Z", data=data,
psGrid=grid, iter=2, seed=10)
summary(out, boot, contrast="te")