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The population-attributable fraction for time-dependent exposures using dynamic prediction and landmarking
Maja von Cube1,2, Martin Schumacher1,2, Hein Putter3
1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
Biometrical Journal. Biometrische Zeitschrift
|June 20, 2019
Summary
This study introduces dynamic prediction and landmarking to accurately estimate the population-attributable fraction (PAF) for time-dependent exposures. This method helps quantify preventable disease burden, improving public health interventions.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- The population-attributable fraction (PAF) quantifies public health impact but faces challenges with time-dependent exposures.
- Defining 'exposed' and 'unexposed' becomes complex when exposure status changes over time.
Purpose of the Study:
- To propose and evaluate novel methods for defining and estimating the PAF in the presence of time-dependent exposures.
- To address the complexities of PAF interpretation and estimation in dynamic health scenarios.
Main Methods:
- Introduction of dynamic prediction and landmarking techniques for PAF estimation.
- Discussion of two distinct estimands based on hypothetical interventions.
- Utilization of generalized-linear models, pseudo-values, and inverse-probability weights for estimation.
- Validation through a simulation study and application to real-world data.
Main Results:
- The proposed methods allow for the definition and estimation of PAF even when exposures vary over time.
- The approach was successfully applied to estimate the population benefit of preventing ventilator-associated pneumonia and to quantify recurrence burden in breast cancer patients.
Conclusions:
- Dynamic prediction and landmarking provide a robust framework for estimating the population-attributable fraction with time-dependent exposures.
- This methodology enhances the ability to quantify preventable disease burden and inform public health strategies.
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