  Recent observations, especially in cancer immunotherapy clinical trials with
time-to-event outcomes, show that the commonly used proportial hazard
assumption is often not justifiable, hampering an appropriate analyse of the
data by hazard ratios. An attractive alternative advocated is given by the
restricted mean survival time (RMST), which does not rely on any model
assumption and can always be interpreted intuitively. As pointed out recently
by Horiguchi and Uno (2020), methods for the RMST based on asymptotic theory
suffer from inflated type-I error under small sample sizes. To overcome this
problem, they suggested a permutation strategy leading to more convincing
results in simulations. However, their proposal requires an exchangeable data
set-up between comparison groups which may be limiting in practice. In
addition, it is not possible to invert their testing procedure to obtain valid
confidence intervals, which can provide more in-depth information. In this
paper, we address these limitations by proposing a studentized permutation test
as well as the corresponding permutation-based confidence intervals. In our
extensive simulation study, we demonstrate the advantage of our new method,
especially in situations with relative small sample sizes and unbalanced
groups. Finally we illustrate the application of the proposed method by
re-analysing data from a recent lung cancer clinical trial.
