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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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The population-attributable fraction for time-to-event data.

Maja von Cube¹, Martin Schumacher¹, Jean Francois Timsit^2,3

¹Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.

International Journal of Epidemiology

|November 22, 2022

Summary

This study clarifies population-attributable fraction (PAF) estimation in complex time-to-event data, reducing bias in competing risks and time-dependent exposures for accurate public health insights.

Keywords:

Attributable fraction competing risks bias immortal time bias multi-state modelling software implementation

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Area of Science:

Epidemiology
Biostatistics
Causal Inference

Background:

Population-attributable fraction (PAF) is frequently misestimated or misinterpreted in clinical and research settings.
Complex time-to-event data amplifies the risk of bias in PAF calculations.

Purpose of the Study:

To define, identify, and estimate PAF in time-to-event settings with competing risks and time-dependent exposures.
To demonstrate methods for mitigating biases such as competing risks, immortal time bias, and confounding.

Main Methods:

Utilized multi-state methodology and inverse probability weighting.
Applied causal and multi-state modeling frameworks for PAF estimation.

Main Results:

Successfully reduced or avoided severe biases in PAF estimation.
Exemplarily applied the method to real-world data, estimating deaths attributable to ventilator-associated pneumonia in France, 2016.
Demonstrated extrapolation of PAF estimates to target populations.

Conclusions:

Unified causal and multi-state modeling provides a robust framework for defining and estimating PAF in advanced time-to-event settings.
The proposed approach effectively addresses common sources of bias.
The methodology allows for straightforward implementation using standard statistical software.