summary.cmprsk {causalCmprsk}

Summary of Event-specific Cumulative Hazards, Cumulative Incidence Functions and Various Treatment Effects

Description

Returns an object of class data.frame containing the summary extracted from the cmprsk object.

Usage

## S3 method for class 'cmprsk'
summary(object, event, estimand = "CIF", ...)

Arguments

object

an object of class cmprsk (output from fit.nonpar or fit.cox functions)

event

an integer number (a code) of an event of interest

estimand

a character string naming the type of estimand to extract from object. estimand can be one of the following: "CumHaz" (Cumulative Hazard function), "CIF" (Cumulative Incidence Function), "RMT" (Restricted Mean Time), "logHR" (logarithm of the ratio of Cumulative Hazards in two treatment arms), "RD" (Risk Difference, or the difference between the CIFs in two treatment arms), "RR" (Risk Ratio, or the ratio of CIFs in two treatment arms), "ATE.RMT" (Restricted mean time gained/lost due to treatment, or the difference between RMTs in two treatment arms). The default value is "CIF".

...

This is not currently used, included for future methods.

Value

summary.cmprsk returns a data.frame object with 7 or 6 columns: the time vector, an indicator of the treatment arm (if the requested estimand is one of c("logHR", "RD", "RR", "ATE.RMT"), this column is omitted), an indicator of the type of event, the point estimate for the requested estimand, the lower and upper bounds of the confidence interval (for conf.level % of the confidence level), and the standard error of the point estimate. For example, if estimand="CIF", the returned data.frame will include the following columns: time, TRT, Event, CIF, CIL.CIF, CIU.CIF, SE.CIF.

References

M.-L. Charpignon, B. Vakulenko-Lagun, B. Zheng, C. Magdamo, B. Su, K.E. Evans, S. Rodriguez, et al. 2022. Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia. Nature Communications 13:7652.

See Also

fit.cox, fit.nonpar, causalCmprsk

Examples

# create a data set
n <- 1000
set.seed(7)
c1 <-  runif(n)
c2 <- as.numeric(runif(n)< 0.2)
set.seed(77)
cf.m.T1 <- rweibull(n, shape=1, scale=exp(-(-1 + 2*c1)))
cf.m.T2 <-  rweibull(n, shape=1, scale=exp(-(1 + 1*c2)))
cf.m.T <- pmin( cf.m.T1, cf.m.T2)
cf.m.E <- rep(0, n)
cf.m.E[cf.m.T1<=cf.m.T2] <- 1
cf.m.E[cf.m.T2<cf.m.T1] <- 2
set.seed(77)
cf.s.T1 <- rweibull(n, shape=1, scale=exp(-1*c1 ))
cf.s.T2 <-  rweibull(n, shape=1, scale=exp(-2*c2))
cf.s.T <- pmin( cf.s.T1, cf.s.T2)
cf.s.E <- rep(0, n)
cf.s.E[cf.s.T1<=cf.s.T2] <- 1
cf.s.E[cf.s.T2<cf.s.T1] <- 2
exp.z <- exp(0.5 + c1 - c2)
pr <- exp.z/(1+exp.z)
TRT <- ifelse( runif(n)< pr, 1, 0)
X <- ifelse(TRT==1, cf.m.T, cf.s.T)
E <- ifelse(TRT==1, cf.m.E, cf.s.E)
covs.names <- c("c1", "c2")
data <- data.frame(X=X, E=E, TRT=TRT, c1=c1, c2=c2)
# Nonparametric estimation:
form.txt <- paste0("TRT", " ~ ", paste0(c("c1", "c2"), collapse = "+"))
trt.formula <- as.formula(form.txt)
res.ATE <- fit.nonpar(df=data, X="X", E="E", trt.formula=trt.formula, wtype="stab.ATE")
# summarizing results on the Risk Difference for event=2
fit.summary <- summary(object=res.ATE, event = 2, estimand="RD")
head(fit.summary)
# summarizing results on the CIFs for event=1
fit.summary <- summary(object=res.ATE, event = 1, estimand="CIF")
head(fit.summary)