This function summarize the results
# S3 method for class 'tteICE'
print(x, digits = 3, ...)A fitted object returned by the function surv.tteICE or scr.tteICE.
The digits of the results
Other augments in function print.default
Print the summary of a tteICE object
## load data
data(bmt)
bmt = transform(bmt, d4=d2+d3)
A = as.numeric(bmt$group>1)
## print the results
for (st in c('composite','natural','removed','whileon','principal')){
fit = surv.tteICE(A, bmt$t2, bmt$d4, st)
print(fit)
}
#> The P-value of the estimated treatment effect by strategy composite using np estimation method: 0.591
#> -------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> Warning: `seed` is supplied but `nboot = 0`; seed will be ignored.
#> 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
#> The P-value of the estimated treatment effect by strategy natural using np estimation method: 0.638
#> -------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> Warning: `seed` is supplied but `nboot = 0`; seed will be ignored.
#> 660 1320 1980 2640
#> CIF1 0.237 0.278 0.278 0.317
#> se1 0.044 0.047 0.047 0.063
#> CIF0 0.311 0.311 0.311 0.311
#> se0 0.078 0.078 0.078 0.078
#> ATE -0.074 -0.033 -0.033 0.006
#> se 0.086 0.087 0.087 0.095
#> p.val 0.389 0.705 0.705 0.946
#> The P-value of the estimated treatment effect by strategy removed using np estimation method: 0.638
#> -------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> Warning: `seed` is supplied but `nboot = 0`; seed will be ignored.
#> 660 1320 1980 2640
#> CIF1 0.280 0.348 0.348 0.417
#> se1 0.050 0.056 0.056 0.084
#> CIF0 0.404 0.404 0.404 0.404
#> se0 0.094 0.094 0.094 0.094
#> ATE -0.125 -0.056 -0.056 0.012
#> se 0.106 0.109 0.109 0.126
#> p.val 0.239 0.607 0.607 0.921
#> The P-value of the estimated treatment effect by strategy whileon using np estimation method: 0.714
#> -------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> Warning: `seed` is supplied but `nboot = 0`; seed will be ignored.
#> 660 1320 1980 2640
#> CIF1 0.239 0.282 0.282 0.324
#> se1 0.043 0.046 0.046 0.062
#> CIF0 0.311 0.311 0.311 0.311
#> se0 0.078 0.078 0.078 0.078
#> ATE -0.072 -0.028 -0.028 0.013
#> se 0.089 0.090 0.090 0.099
#> p.val 0.419 0.754 0.754 0.898
#> The P-value of the estimated treatment effect by strategy principal using np estimation method: NA
#> -------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> Warning: `seed` is supplied but `nboot = 0`; seed will be ignored.
#> 660 1320 1980 2640
#> CIF1 0.344 0.407 0.407 0.466
#> se1 0.058 0.061 0.061 0.083
#> CIF0 0.462 0.462 0.462 0.462
#> se0 0.103 0.103 0.103 0.103
#> ATE -0.117 -0.055 -0.055 0.005
#> se 0.118 0.120 0.120 0.132
#> p.val 0.320 0.648 0.648 0.972
for (st in c('composite','natural','removed','whileon','principal')){
fit = scr.tteICE(A, bmt$t1, bmt$d1, bmt$t2, bmt$d2, st)
print(fit, digits=2)
}
#> The P-value of the estimated treatment effect by strategy composite using np estimation method: 0.59
#> -------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> Warning: `seed` is supplied but `nboot = 0`; seed will be ignored.
#> 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
#> The P-value of the estimated treatment effect by strategy natural using np estimation method: 0.5
#> -------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> Warning: `seed` is supplied but `nboot = 0`; seed will be ignored.
#> 551 1102 1653 2204
#> CIF1 0.46 0.55 0.57 0.62
#> se1 0.06 0.06 0.06 0.07
#> CIF0 0.55 0.61 0.64 0.64
#> se0 0.08 0.08 0.08 0.08
#> ATE -0.09 -0.06 -0.07 -0.01
#> se 0.08 0.08 0.08 0.08
#> p.val 0.27 0.46 0.34 0.86
#> The P-value of the estimated treatment effect by strategy removed using np estimation method: 0.64
#> -------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> Warning: `seed` is supplied but `nboot = 0`; seed will be ignored.
#> 660 1320 1980 2640
#> CIF1 0.28 0.35 0.35 0.42
#> se1 0.05 0.06 0.06 0.08
#> CIF0 0.40 0.40 0.40 0.40
#> se0 0.09 0.09 0.09 0.09
#> ATE -0.12 -0.06 -0.06 0.01
#> se 0.11 0.11 0.11 0.13
#> p.val 0.24 0.61 0.61 0.92
#> The P-value of the estimated treatment effect by strategy whileon using np estimation method: 0.71
#> -------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> Warning: `seed` is supplied but `nboot = 0`; seed will be ignored.
#> 660 1320 1980 2640
#> CIF1 0.24 0.28 0.28 0.32
#> se1 0.04 0.05 0.05 0.06
#> CIF0 0.31 0.31 0.31 0.31
#> se0 0.08 0.08 0.08 0.08
#> ATE -0.07 -0.03 -0.03 0.01
#> se 0.09 0.09 0.09 0.10
#> p.val 0.42 0.75 0.75 0.90
#> The P-value of the estimated treatment effect by strategy principal using np estimation method: NA
#> -------------------------------------------------------------------
#> The estimated cumulative incidences and treatment effects at quartiles:
#> Warning: `seed` is supplied but `nboot = 0`; seed will be ignored.
#> 660 1320 1980 2640
#> CIF1 0.34 0.41 0.41 0.47
#> se1 0.06 0.06 0.06 0.08
#> CIF0 0.46 0.46 0.46 0.46
#> se0 0.10 0.10 0.10 0.10
#> ATE -0.12 -0.05 -0.05 0.00
#> se 0.12 0.12 0.12 0.13
#> p.val 0.32 0.65 0.65 0.97