Can we detect treatment effect waning from time-to-event data?

TL;DR

This paper investigates the challenges of detecting treatment effect waning over time using time-to-event data, highlighting the limitations of current methods and the need for strong assumptions to identify waning.

Contribution

It critically analyzes existing approaches to assess treatment effect waning and demonstrates the fundamental limitations without strong modeling assumptions.

Findings

Standard survival comparisons do not reveal waning effects.

Causal hazard ratios can be misleading regarding waning.

Waning cannot be reliably identified without strong assumptions.

Abstract

Understanding how the causal effect of a treatment evolves over time, including the potential for waning, is important for informed decisions on treatment discontinuation or repetition. For example, waning vaccine protection influences booster dose recommendations, while cost-effectiveness analyses require accounting for long-term efficacy of treatments. However, there is no consensus on the methodology to assess and account for treatment effect waning. Even in randomized controlled trials, the common na\"ive comparison of hazard functions can lead to misleading causal conclusions due to inherent selection bias. Although comparing survival curves is sometimes recommended as a safer measure of causal effect, it only represents a cumulative effect over time and does not address treatment effect waning. We also explore recent formulations of causal hazard ratios, based on the principal stratification approach or the controlled direct effect. These causal hazard ratios cannot be identified without strong modeling assumptions, but bounds can be derived accounting for unobserved heterogeneity and one could try to use them to detect treatment effect waning. However, we illustrate that an increase in causal hazard ratios towards one does not necessarily mean that the protective effect of the treatment is fading. Furthermore, the same survival functions may correspond to both scenarios with and without waning, which shows that treatment effect waning cannot be identified from standard time-to-event data without strong untestable modeling assumptions.