This function compares survival curves as modeled with do_kpm().
It outputs a contingency table and a Chisq measure of difference.
Arguments
- kds
data set of a survival model such as
do_kpm()- cond
character of experimental condition variable in the data
- test_type
numeric (0 or 1) parameter that controls the type of test (0 means rho = 0; log-rank, 1 means rho = 1; Peto & Peto Wilcox)
Value
Returns survival test results as called from survival::survdiff().
Examples
kpm_est <- do_kpm(add_dropout_idx(dropRdemo, 3:54))
get_survdiff(kpm_est$d, "experimental_condition", 0)
#> Call:
#> survdiff(formula = f, data = kds, rho = test_type)
#>
#> N Observed Expected (O-E)^2/E (O-E)^2/V
#> experimental_condition=11 72 27 27.6 0.0148 0.0212
#> experimental_condition=12 57 15 23.6 3.1164 4.2203
#> experimental_condition=21 56 23 21.2 0.1505 0.1972
#> experimental_condition=22 61 29 21.6 2.5533 3.3664
#>
#> Chisq= 5.9 on 3 degrees of freedom, p= 0.1
get_survdiff(kpm_est$d, "experimental_condition", 1)
#> Call:
#> survdiff(formula = f, data = kds, rho = test_type)
#>
#> N Observed Expected (O-E)^2/E (O-E)^2/V
#> experimental_condition=11 72 22.1 22.6 0.00792 0.0137
#> experimental_condition=12 57 12.2 19.1 2.54844 4.1614
#> experimental_condition=21 56 18.7 17.3 0.10125 0.1596
#> experimental_condition=22 61 23.9 17.8 2.08041 3.2905
#>
#> Chisq= 5.8 on 3 degrees of freedom, p= 0.1