Transportability of Prognostic Markers: Rethinking Common Practices through a Sufficient-Component-Cause Perspective

TL;DR

This paper reexamines the transportability of prognostic markers across populations using a sufficient component causes framework, highlighting the importance of causal cause distributions and proposing new transportation methods based on cause variability.

Contribution

It introduces a causal SCC perspective to assess and improve the transportability of prognostic markers, challenging traditional assumptions and proposing new algorithms.

Findings

Different transportability assumptions affect information loss.

Cause distribution stability is crucial for marker transportability.

Proposed methods reflect varying knowledge about cause variability.

Abstract

Transportability, the ability to maintain performance across populations, is a desirable property of markers of clinical outcomes. However, empirical findings indicate that markers often exhibit varying performances across populations. For prognostic markers that are advertised as predictive risk equations, oftentimes a form of updating is required when the equation is transported to populations with different disease prevalences. Here, we revisit transportability of prognostic markers through the lens of the foundational framework of sufficient component causes (SCC). We argue that transporting a marker 'as is' implicitly assumes predictive values are transportable, whereas conventional prevalence adjustment shifts the locus of transportability to accuracy metrics (sensitivity and specificity). Using a minimalist SCC framework that decomposes risk prediction into its causal constituents, we show that both approaches rely on strong assumptions about the stability of cause distributions. A SCC framework instead invites making transparent assumptions about how different causes vary across populations, leading to different transportation methods. For example, in the absence of any external information other than disease prevalence, a cause-neutral perspective can assume all causes are responsible for change in prevalence, leading to a new form of marker transportation. Numerical experiments demonstrate that different transportability assumptions lead to varying degrees of information loss, depending on the distribution of causes. A SCC perspective challenges common assumptions and practices for marker transportability, and proposes transportation algorithms that reflect our knowledge or assumptions about how causes vary across populations.