Using Principal Progression Rate to Quantify and Compare Disease Progression in Comparative Studies

TL;DR

This paper introduces Principal Progression Rate (PPR), a new estimand for disease progression studies that captures the trajectory's dynamics more effectively than mean change measures, enhancing statistical power.

Contribution

The paper proposes PPR as a flexible, weighted average of the trajectory's slope, improving upon mean CFB by better utilizing longitudinal data and increasing power in comparative studies.

Findings

PPR can outperform mean CFB in detecting treatment effects.

Properly chosen PPR enhances statistical power and estimation precision.

Real data analysis demonstrates PPR's advantages over traditional methods.

Abstract

In comparative studies of progressive diseases, such as randomized controlled trials (RCTs), the mean Change From Baseline (CFB) of a continuous outcome at a pre-specified follow-up time across subjects in the target population is a standard estimand used to summarize the overall disease progression. Despite its simplicity in interpretation, the mean CFB may not efficiently capture important features of the trajectory of the mean outcome relevant to the evaluation of the treatment effect of an intervention. Additionally, the estimation of the mean CFB does not use all longitudinal data points. To address these limitations, we propose a class of estimands called Principal Progression Rate (PPR). The PPR is a weighted average of local or instantaneous slope of the trajectory of the population mean 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 the signal of treatment effect and/or improving estimation precision. We evaluated different versions of PPRs and the performance of their estimators through numerical studies. A real dataset was analyzed to demonstrate the advantage of using alternative PPR over the mean CFB.