# The population-attributable fraction for time-dependent exposures using dynamic prediction and landmarking

## TL;DR

This paper introduces a novel method using dynamic prediction and landmarking to accurately estimate the population-attributable fraction for time-dependent exposures, addressing challenges in defining exposure over time.

## Contribution

It proposes a new framework for estimating PAF with time-dependent exposures, using two hypothetical intervention-based estimands and applicable estimation techniques.

## Key findings

- Method performs well in simulation studies.
- Applied to ICU data for pneumonia prevention.
- Quantifies cancer recurrence burden.

## Abstract

The public health impact of a harmful exposure can be quantified by the population-attributable fraction (PAF). The PAF describes the attributable risk due to an exposure and is often interpreted as the proportion of preventable cases if the exposure could be extinct. Difficulties in the definition and interpretation of the PAF arise when the exposure of interest depends on time. Then, the definition of exposed and unexposed individuals is not straightforward. We propose dynamic prediction and landmarking to define and estimate a PAF in this data situation. Two estimands are discussed which are based on two hypothetical interventions that could prevent the exposure in different ways. Considering the first estimand, at each landmark the estimation problem is reduced to a time-independent setting. Then, estimation is simply performed by using a generalized-linear model accounting for the current exposure state and further (time-varying) covariates. The second estimand is based on counterfactual outcomes, estimation can be performed using pseudo-values or inverse-probability weights. The approach is explored in a simulation study and applied on two data examples. First, we study a large French database of intensive care unit patients to estimate the population-benefit of a pathogen-specific intervention that could prevent ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa. Moreover, we quantify the population-attributable burden of locoregional and distant recurrence in breast cancer patients.