The population‐attributable fraction for time‐dependent exposures and competing risks—A discussion on estimands

Abstract

The population-attributable fraction (PAF) quantifies the public health impact of a harmful exposure. Despite being a measure of significant importance, an estimand accommodating complicated time-to-event data is not clearly defined.We discuss current estimands of the PAF used to quantify the public health impact of an internal time-dependent exposure for data subject to competing outcomes. To overcome some limitations, we proposed a novel estimand that is based on dynamic prediction by landmarking. In a profound simulation study, we discuss interpretation and performance of the various estimands and their estimators. The methods are applied to a large French database to estimate the health impact of ventilator-associated pneumonia for patients in intensive care.

Attributable Mortality of Ventilator-associated Pneumonia. Replicating Findings, Revisiting Methods

“…In this study, we aimed to replicate earlier findings of limited VAP-attributable ICU mortality (13) and, in doing so, illustrate the importance of appropriately accounting for the timedependent nature of the events under study and their confounding factors.…”

“…The aim of this article is twofold. First, we aim to replicate earlier findings from Bekaert and colleagues (13), who provided the first and (to our knowledge) only study to appropriately address time-dependent confounding in estimation of VAP-attributable ICU mortality. We assess whether their findings of limited VAP-attributable ICU mortality, based on the French multicenter Outcomerea database (n = 4,479) (1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008), generalize to an independent cohort of 2,720 mechanically ventilated patients.…”

“…Approach 4 is an extension of approach 3 in which informative censoring of infected patients (or, equivalently, time-dependent confounding of the effect of VAP) is accounted for by additionally incorporating patient (covariate) history into calculation of the IP weights. This approach is identical to previously suggested causal inference techniques based on IP weighting (13,(22)(23)(24)(25) and involves a more elaborate modelling component (due to adjustment for timedependent covariates). In particular, a Cox proportional hazards model was fitted for the daily probability of acquiring VAP in function of the available covariate history, including admission characteristics and time-dependent factors as listed in the section 'Data Collection'.…”

“…Our smaller estimates may also in part reflect a smaller excess risk of death in patients with a poorer prognosis (13,31,32).…”

“…Although largely in line with the modest impact reported by Bekaert and colleagues (13) (4.4% on day 30 and 5.9% on day 60), we found an even more limited impact. Between-study variability in estimates may be related to a host of factors, such as differences in patient casemix, diagnostic procedures and definitions, characteristics of infection, timeliness and appropriateness of treatment, application of different (and often suboptimal) statistical methods and use of various definitions of excess risk (5,13). In keeping with (13), we have chosen to focus on the PAF because of its intuitively appealing interpretation and its central role as a target of inference of predominant MSM-based approaches (8).…”