Using principal progression rate to quantify and compare disease progression in comparative studies

In comparative studies of progressive diseases, such as randomized controlled trials (RCTs), mean Change From Baseline (CFB) of a continuous outcome at a pre-specified follow-up time in the target population is a standard estimand for summarizing overall disease progression. While easy to interpret, the mean CFB may not efficiently capture important features of the mean outcome trajectory relevant to the treatment effect evaluation. Additionally, the estimation of the mean CFB does not use all longitudinal data. To address these limitations, we propose a class of estimands called Principal Progression Rate (PPR), which is a weighted average of local or instantaneous slope of the mean outcome trajectory during the follow-up. The flexibility of the weight function allows the PPR to cover a broad class of intuitive estimands, including the mean CFB, the slope of ordinary least-square fit to the trajectory, and the area under the curve. We showed that properly chosen PPRs can enhance statistical power over the mean CFB by amplifying treatment effect signal and/or improving estimation precision. We evaluated different versions of PPRs and their estimators through numerical studies, and applied them to the dataset of the EMERGE clinical trial, demonstrating the advantage of using alternative PPR over the mean CFB.