summary.riskCurve {pssmooth}

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 riskCurve, typically returned by riskCurve

boot

an object returned by bootRiskCurve. If NULL (default), only point estimates are reported.

contrast

a character string specifying the mCEP curve. It must be one of te (treatment efficacy), rr (relative risk), logrr (log relative risk), and rd (risk difference [placebo minus treatment]).

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

riskCurve and bootRiskCurve

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")