Background
ICH
DISEASE, EVENT
Randomised clinical trials are often designed using a time-to-event variable as primary outcome, especially in disease areas (e.g. cancer)When designing a trial, it is important to carefully define the estimand(s) of interest. In the framework recently proposed by ICH,The HR is defined as the ratio of hazard rates between the treatment and control arms. Its interpretation is clear under the assumption that the hazards in the two groups are proportional over time. However, this assumption is often not reasonable, for example when the hazards in two groups only differ at early, or later, stages of follow-up.When interest is in an absolute contrast between the arms or when the proportional hazards assumption does not hold, alternative summary measures might be preferable. Promising summary measures include difference in restricted mean survival time (DRMST),In non-inferiority trials, rather than testing whether an active treatment is better than a control one, we aim to show that it is not worse by a certain amount (known as the non-inferiority margin) or more; given ancillary advantages of this active treatment, this is then considered enough to recommend its use. The margin is defined on the scale of the chosen summary measure.In superiority trials, if assuming proportional hazards, the null hypotheses for different summary measures are the same across the event rate range, so choice of summary measure does not affect power. However, this is not true for non-inferiority trials. As a consequence, a test based on DRMST is often more powerful for the same sample size than one based on the HR. Uno et al.The aim of this article is twofold: first, we introduce and evaluate a method of estimating DRMST and DS under the proportional hazards assumption using flexible parametric survival models, comparing the power of this estimation method to that of standard non-parametric methods. Second, we test through simulation the above hypothesis about the superior power of DRMST, by estimating different summary measures under the same flexible parametric survival model and comparing with HR in terms of power, type I error and interpretation of results. We include simulations for superiority trials to show that power gains are unique to the non-inferiority setting.