Estimation of the attributable fraction for time to event outcomes using an inverse probability of exposure weighted Kaplan-Meier estimator

TL;DR

This paper introduces a new, simple estimator for population attributable fractions in time-to-event data, addressing issues like censoring and confounding with an inverse probability weighted Kaplan-Meier approach.

Contribution

The paper proposes a novel estimator for attributable fractions that is easy to implement and valid for censored and confounded time-to-event data, using inverse probability weighting and bootstrap inference.

Findings

The estimator performs well in simulations, showing low bias.

Traditional methods exhibit bias in various scenarios.

The proposed method remains valid under non-proportional hazards.

Abstract

Population attributable fractions aim to quantify the proportion of the cases of an outcome (for example, a disease) that would have been avoided had no individuals in the population been exposed to a given exposure. This quantity thus plays a crucial role in epidemiology and public health, notably to guide policies, interventions or to assess the burden of a disease due to a particular exposure. Various statistical methods have been proposed to estimate attributable fractions using observational data. When time-to-event data are used, several of these formulas yield invalid results. Alternative valid formulas are available but remain scarcely used. We propose a new estimator of the attributable fraction that is both conceptually simple and easy to implement using common statistical software. Our proposed estimator makes use of the Kaplan-Meier estimator to address censoring and potentially non-proportional hazards, as well as inverse probability weighting to control confounding. Nonparametric bootstrap is proposed to produce inferences. A simulation study is used to illustrate and compare our proposed estimator to several alternatives. The results showcase the bias of many commonly used traditional approaches and the validity of our estimator under its working assumptions.