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 or if the same distribution is assumed across time or space. Nonparametric methods to estimate the potential impact fraction are available for cohort data, but no alternatives exist for cross-sectional data. In this article, we discuss the impact of distributional assumptions in the estimation of the population impact fraction, showing that under an infinite set of possibilities, distributional violations lead to biased estimates. We propose nonparametric methods to estimate the potential impact fraction for aggregated (mean and standard deviation) or individual data (e.g. observations from a cross-sectional population survey), and develop simulation scenarios to compare their performance against standard parametric procedures. We illustrate our methodology on an application of sugar-sweetened beverage consumption on incidence of type 2 diabetes. We also present an R package pifpaf to implement these methods.
翻译:连续接触数据的潜在影响部分(包括人口可归属部分)的估计往往依赖于强有力的分布假设,然而,如果基本接触分布不明,或者假设不同时间或空间的分布相同,则这些假设往往被违反。集体数据有非对称方法可以估计潜在影响部分,但跨部门数据没有替代方法。在本条中,我们讨论了分配假设对估计人口影响部分的影响,表明在一系列无限的可能性下,分配违反会导致偏差估计。我们提出了非对称方法,用以估计总和(平均和标准偏差)或个人数据的潜在影响部分(例如跨部门人口调查的意见),并制订模拟假设,对照标准的参数比较其性能。我们介绍了在2类糖尿病发生时使用糖湿饮料消费的方法。我们还提供了用于实施这些方法的R包Pifpaf。