Handling time-dependent exposures and confounders when estimating attributable fractions -- bridging the gap between multistate and counterfactual modeling

TL;DR

This paper explores how to accurately estimate population-attributable fractions in dynamic settings with time-dependent exposures and confounders, bridging multistate and g-method modeling approaches to improve bias correction.

Contribution

It provides a weighting-based framework connecting multistate models and g-methods, highlighting current limitations and proposing modifications for better PAF estimation in complex scenarios.

Findings

Identifies shortcomings of existing multistate approaches in handling time-dependent confounding.

Provides a weighting-based characterization linking multistate models and g-methods.

Offers R code to facilitate implementation of improved PAF estimation methods.

Abstract

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.