Nonparametric Estimation of the Potential Impact Fraction and the Population Attributable Fraction With Individual-Level and Aggregated Data
Por:
Chan C.E., Zepeda-Tello R., Camacho-García-Formentí D., Cudhea F., Meza R., Rodrigues E., Spiegelman D., Barrientos-Gutierrez T., Zhou X.
Publicada:
1 ene 2025
Resumen:
The estimation of the potential impact fraction, including the population attributable fraction, with continuous exposure data frequently relies on strong distributional assumptions. However, these assumptions are often violated if the underlying exposure distribution is unknown. In this article, we discuss the impact of distributional assumptions in the estimation of the population impact fraction, showing that distributional violations lead to biased estimates. We propose nonparametric methods to estimate the potential impact fraction for aggregated data, where only the exposure mean and standard deviation are available, or individual data, where the full exposure distribution can be estimated from a sample of the target population. The finite sample performance of the proposed methods is demonstrated through simulation studies. We illustrate our methodology with a study of the impact of eliminating sugar-sweetened beverage consumption on the incidence of type 2 diabetes in Mexico. We also developed the R package pifpaf to implement these methods. © 2025 John Wiley & Sons Ltd.
Filiaciones:
Department of Statistics and Data Science, Yale University, New Haven, CT, United States
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
National Institute of Public Health of Mexico, Cuernavaca, Mexico
Friedman School of Nutrition Science and Policy, Tufts University, Medford, MA, United States
Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States
Instituto de Matemáticas, Universidad Nacional Autónoma de México, México, Mexico
Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
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