# Summary of Point and Interval Estimation of a Marginal Causal Effect Predictiveness Curve 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). ```r ## 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 ## Returns A data frame containing point and possibly interval estimates of the specified mCEP curve. ## Examples ```r 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") ``` ## 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](https://doi.org/10.1093/biostatistics/kxy074). ## See Also `riskCurve` and `bootRiskCurve`

summary.riskCurve function