This function summarizes the results
Usage
# S3 method for class 'tteICE'
print(x, digits = 4, ...)Arguments
- x
A fitted object returned by the function
tteICE,surv.tteICE, orscr.tteICE.- digits
The digits of the results
- ...
Other arguments in function
print.default
Examples
## load data
data(bmt)
bmt = transform(bmt, d4=d2+d3)
A = as.numeric(bmt$group>1)
bmt$A = A
## print the results
fit1 = surv.tteICE(A, bmt$t2, bmt$d4, "composite")
print(fit1)
#> Input:
#> surv.tteICE(A = A, Time = bmt$t2, cstatus = bmt$d4, strategy = "composite")
#> -----------------------------------------------------------------------
#> Data type: competing risks
#> Strategy: composite variable strategy
#> Estimation method: nonparametric estimation
#> Observations: 137 (including 99 treated and 38 control)
#> Maximum follow-up time: 2640
#> P-value of the average treatment effect: 0.5907
#> -----------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> 660 1320 1980 2640
#> CIF1 0.5323 0.5864 0.5864 0.6299
#> se1 0.0501 0.0499 0.0499 0.0617
#> CIF0 0.6087 0.6377 0.6377 0.6377
#> se0 0.0803 0.0793 0.0793 0.0793
#> ATE -0.0764 -0.0513 -0.0513 -0.0078
#> se 0.0946 0.0937 0.0937 0.1005
#> p.val 0.4192 0.5843 0.5843 0.9384
#>
fit2 = scr.tteICE(A, bmt$t1, bmt$d1, bmt$t2, bmt$d2, "composite")
print(fit2, digits=2)
#> Input:
#> scr.tteICE(A = A, Time = bmt$t1, status = bmt$d1, Time_int = bmt$t2,
#> status_int = bmt$d2, strategy = "composite")
#> -----------------------------------------------------------------------
#> Data type: semicompeting risks
#> Strategy: composite variable strategy
#> Estimation method: nonparametric estimation
#> Observations: 137 (including 99 treated and 38 control)
#> Maximum follow-up time: 2640
#> P-value of the average treatment effect: 0.59
#> -----------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> 660 1320 1980 2640
#> CIF1 0.53 0.59 0.59 0.63
#> se1 0.05 0.05 0.05 0.06
#> CIF0 0.61 0.64 0.64 0.64
#> se0 0.08 0.08 0.08 0.08
#> ATE -0.08 -0.05 -0.05 -0.01
#> se 0.09 0.09 0.09 0.10
#> p.val 0.42 0.58 0.58 0.94
#>
library(survival)
fit3 = tteICE(Surv(t2, d4, type = "mstate")~A,
data=bmt, strategy="composite", method='eff')
print(fit3, digits=3)
#> Input:
#> tteICE(formula = Surv(t2, d4, type = "mstate") ~ A, data = bmt,
#> strategy = "composite", method = "eff")
#> -----------------------------------------------------------------------
#> Data type: competing risks
#> Strategy: composite variable strategy
#> Estimation method: semiparametrically efficient estimation
#> Observations: 137 (including 99 treated and 38 control)
#> Maximum follow-up time: 2640
#> P-value of the average treatment effect: 0.591
#> -----------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> 660 1320 1980 2640
#> CIF1 0.532 0.586 0.586 0.630
#> se1 0.050 0.050 0.050 0.062
#> CIF0 0.609 0.638 0.638 0.638
#> se0 0.080 0.079 0.079 0.079
#> ATE -0.076 -0.051 -0.051 -0.008
#> se 0.095 0.094 0.094 0.101
#> p.val 0.419 0.584 0.584 0.938
#>