Analyse the Dataset using the difference in RMST

## Details

The implementation from the nph package is used, see the documentation there for details.

`alternative`

can be "two.sided" for a two sided test of equality of the
summary statistic or "one.sided" for a one sided test testing H0: treatment
has equal or shorter survival than control vs. H1 treatment has longer
survival than control.

The data.frame returned by the created function includes the follwing columns:

`p`

p value of the test, see Details`alternative`

the alternative used`rmst_diff`

estimated differnce in RMST`rmst_diff_lower`

unadjusted lower bound of the confidence interval for differnce in RMST`rmst_diff_upper`

unadjusted upper bound of the confidence interval for differnce in RMST`CI_level`

the CI level used`N_pat`

number of patients`N_evt`

number of events

## Examples

```
condition <- merge(
assumptions_delayed_effect(),
design_fixed_followup(),
by = NULL
) |>
head(1)
dat <- generate_delayed_effect(condition)
analyse_rmst_diff()(condition, dat)
#> $p
#> [1] 0.02254084
#>
#> $alternative
#> [1] "two.sided"
#>
#> $rmst_diff
#> [1] 297.9007
#>
#> $rmst_diff_lower
#> [1] 41.94202
#>
#> $rmst_diff_upper
#> [1] 553.8594
#>
#> $CI_level
#> [1] 0.95
#>
#> $N_pat
#> [1] 300
#>
#> $N_evt
#> [1] 300
#>
```