Advancing Waterfall Plots for Cancer Treatment Response Assessment through Adjustment of Incomplete Follow-Up Time

TL;DR

This paper introduces a novel statistical adjustment method for waterfall plots in oncology, enabling more accurate comparisons between ongoing and completed studies by accounting for incomplete follow-up data.

Contribution

It proposes a survival analysis-based adjustment technique that projects ongoing study waterfall plots to resemble those with sufficient follow-up, improving efficacy assessment.

Findings

Adjusted waterfall plots align more closely with completed study results

Method enables comparison without individual control data

Real-data example demonstrates improved evaluation accuracy

Abstract

Waterfall plots are a key tool in early phase oncology clinical studies for visualizing individual patients' tumor size changes and provide efficacy assessment. However, comparing waterfall plots from ongoing studies with limited follow-up to those from completed studies with long follow-up is challenging due to underestimation of tumor response in ongoing patients. To address this, we propose a novel adjustment method that projects the waterfall plot of an ongoing study to approximate its appearance with sufficient follow-up. Recognizing that waterfall plots are simply rotated survival functions of best tumor size reduction from the baseline (in percentage), we frame the problem in a survival analysis context and adjust weight of each ongoing patients in an interim look Kaplan-Meier curve by leveraging the probability of potential tumor response improvement (i.e., "censoring"). The probability of improvement is quantified through an incomplete multinomial model to estimate the best tumor size change occurrence at each scan time. The adjusted waterfall plots of experimental treatments from ongoing studies are suitable for comparison with historical controls from completed studies, without requiring individual-level data of those controls. A real-data example demonstrates the utility of this method for robust efficacy evaluations.