This function predicts the potential cumulative incidence function and treatment effect at specific time points.
riskpredict(fit, timeset = NULL, nboot = 0, seed = 0)A fitted object returned by the function surv.tteICE or scr.tteICE.
Time at which to predict the risk. If timeset=NULL, risks will be predict at the
quartiles of the maximum follow-up time.
Number of resampling in bootstrapping. By default, nboot = 0, meaning no bootstrap is performed and the standard error is computed using the explicit analytical formula.
Sets the random seed used when generating bootstrap samples.
A matrix. The meanings of each row are: time points, potential cumulative incidences (under treated and under control), treatment effects, standard errors, and P-values.
## load data
data(bmt)
bmt = transform(bmt, d4=d2+d3)
A = as.numeric(bmt$group>1)
X = as.matrix(bmt[,c('z1','z3','z5')])
## Composite variable strategy,
## nonparametric estimation without covariates
fit1 = scr.tteICE(A, bmt$t1, bmt$d1, bmt$t2, bmt$d2, "composite")
riskpredict(fit1, timeset=c(670,2000))
#> Warning: `seed` is supplied but `nboot = 0`; seed will be ignored.
#> 670 2000
#> CIF1 0.53226259 0.58641246
#> se1 0.05012612 0.04988569
#> CIF0 0.63767315 0.63767315
#> se0 0.07929563 0.07929563
#> ATE -0.10541056 -0.05126070
#> se 0.09381057 0.09368233
#> p.val 0.26116016 0.58425802