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 whose results are used to quantify of the risk of an outcome, oftentimes a form of updating is required when the marker 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 across populations. 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 how population differ from each other in 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属性。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。对于用于量化结局风险的预后标志物，当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的视角，重新审视预后标志物的可迁移性问题。我们认为，直接‘原样’迁移标志物隐含地假设了预测值具有可迁移性，而传统的患病率调整则将可迁移性的焦点转移至准确性指标（敏感性和特异性）。通过一个将风险预测分解为其因果构成要素的简约充分组分因果框架，我们证明这两种方法都依赖于关于病因分布在人群间稳定性的强假设。充分组分因果框架则提倡对病因如何在不同人群间变化做出透明假设，从而导向不同的迁移方法。例如，在除疾病患病率外无任何外部信息的情况下，病因中性的视角可以假设所有病因共同导致患病率变化，从而推导出一种新的标志物迁移形式。数值实验表明，不同的可迁移性假设会导致不同程度的信息损失，具体取决于人群间病因分布的差异程度。充分组分因果视角挑战了关于标志物可迁移性的常见假设与实践，并提出了能反映我们关于病因跨人群变化的知识或假设的迁移算法。