Meta TCT: Towards Interpretable Treatment Effects in Clinical Trials for Progressive Diseases

In progressive diseases, like Alzheimer's disease, treatments that slow progression should start early in the disease course to longer maintain higher levels of functioning. In corresponding clinical trials, the treatment effect is usually expressed in terms of mean differences on a clinical scale. Early in the disease course, however, treatment effects expressed on a clinical scale are often small but may nonetheless correspond to an important slowing of disease progression. This complicates the appreciation of the relevance of observed treatment effects. For example, it may be difficult to determine whether a 2-point improvement on a clinical scale is relevant for clinical practice. In this paper, we propose the meta Time-Component Tests (meta TCT). This new approach leads to estimators of treatment effects on the time scale, in terms of time saved or percentage slowing of progression, that are easy to interpret. This approach is based on estimates obtained from an arbitrary model for longitudinal data and is, therefore, very flexible. Asymptotic properties of the Meta TCT estimators are derived and evaluated in an extensive simulation study. Meta TCT is then applied to a phase 2/3 clinical trial for Alzheimer's disease, which was first analyzed with a mixed model. In this trial, meta TCT leads to important additional insights into the treatment effect. We believe that meta TCT will facilitate the estimation of interpretable treatment effects in clinical trials for progressive diseases, and that this, in turn, will fine-tune the evaluation of the clinical relevance of new treatments.