Transportability, the ability to maintain performance across populations, is a desirable property of 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.

翻译：可迁移性（即在不同人群中保持性能的能力）是临床结局标志物的理想特性。然而实证研究表明，标志物在不同人群中常表现出性能差异。对于作为预测风险方程发布的预后标志物，当方程迁移至疾病患病率不同的人群时，通常需要进行某种形式的更新。本文通过充分组分因果这一基础框架重新审视预后标志物的可迁移性问题。我们认为，直接迁移标志物隐含着预测值具有可迁移性的假设，而传统的患病率调整则将可迁移性焦点转移至准确度指标（敏感性与特异性）。通过将风险预测分解为因果成分的简约化充分组分因果框架，我们证明这两种方法均依赖于对病因分布稳定性的强假设。充分组分因果框架则提倡明确假设不同病因在人群间的变异方式，从而衍生出不同的迁移方法。例如，在仅掌握疾病患病率而缺乏其他外部信息时，可采取病因中性视角假设所有病因共同导致患病率变化，由此产生新型标志物迁移方法。数值实验表明，根据病因分布差异，不同的可迁移性假设会导致不同程度的信息损失。充分组分因果视角对标志物可迁移性的常见假设与实践提出挑战，并提出能反映我们对病因跨人群变异认知或假设的迁移算法。