The population-attributable fraction (PAF) expresses the proportion of events that can be ascribed to a certain exposure in a certain population. It can be strongly time-dependent because either exposure incidence or excess risk may change over time. Competing events may moreover hinder the outcome of interest from being observed. Occurrence of either of these events may, in turn, prevent the exposure of interest. Estimation approaches therefore need to carefully account for the timing of events in such highly dynamic settings. The use of multistate models has been widely encouraged to eliminate preventable yet common types of time-dependent bias. Even so, it has been pointed out that proposed multistate modeling approaches for PAF estimation fail to fully eliminate such bias. In addition, assessing whether patients die from rather than with a certain exposure not only requires adequate modeling of the timing of events but also of their confounding factors. While proposed multistate modeling approaches for confounding adjustment may adequately accommodate baseline imbalances, unlike g-methods, these proposals are not generally equipped to handle time-dependent confounding. However, the connection between multistate modeling and g-methods (e.g. inverse probability of censoring weighting) for PAF estimation is not readily apparent. In this paper, we provide a weighting-based characterization of both approaches to illustrate this connection, to pinpoint current shortcomings of multistate modeling, and to enhance intuition into simple modifications to overcome these. R code is made available to foster the uptake of g-methods for PAF estimation.

翻译：人群归因分数（PAF）表示特定人群中可归因于特定暴露的事件比例。由于暴露发生率或超额风险可能随时间变化，PAF可能呈现强时间依赖性。此外，竞争事件可能阻碍目标结局的观测，而这两类事件的发生反过来也可能阻止目标暴露的发生。因此，在这种高度动态的背景下，估计方法需要仔细考虑事件的发生时机。多状态模型被广泛倡导以消除常见但可预防的时间依赖性偏倚。尽管如此，有学者指出，现有用于PAF估计的多状态建模方法未能完全消除此类偏倚。此外，评估患者是因特定暴露死亡还是仅伴随暴露死亡，不仅需要充分建模事件发生时机，还需考虑混杂因素。虽然针对混杂调整的多状态建模方法可能充分处理基线失衡，但与g方法不同，这些方法通常无法处理时间依赖性混杂。然而，多状态模型与g方法（如逆概率删失加权）在PAF估计中的关联并不直观。本文基于加权方法对两种建模方法进行刻画，以阐明这种关联，明确多状态建模的现有不足，并增强对简单改进措施以克服这些局限的直觉理解。文中提供R代码以促进g方法在PAF估计中的应用。