The proportional treatment effect: A metric that empowers and connects

Clinical trials with continuous endpoints evaluate efficacy by comparing the difference in mean changes from baseline between groups. However, clinicians often interpret results in terms of a proportional reduction rather than an absolute difference. An alternative approach is to reparametrize this difference as a proportional treatment effect (PTE), calculated by dividing the difference by the placebo mean change. PTE is not a new metric per se, but a specific reparameterization gaining traction in certain clinical contexts. We demonstrate that, in theory, PTE can be more powerful than the simple difference in means while still controlling the type I error rate. This is achieved using the delta method, as implemented in well-established computational tools like the R package 'msm' and the SAS procedure 'NLMIXED'. By analyzing data from phase III trials, we illustrate how a PTE connects treatment outcomes across various endpoints and different presentation formats. The availability of these well-established statistical tools for estimating proportional treatment effects, combined with this theoretical demonstration, suggests an alternative test statistic for clinical trials with continuous endpoints.