Related Experiment Video
Updated: Sep 11, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Nonparametric Estimation of the Potential Impact Fraction and the Population Attributable Fraction With
Colleen E Chan1, Rodrigo Zepeda-Tello2, Dalia Camacho-García-Formentí3
1Department of Statistics and Data Science, Yale University, New Haven, Connecticut, USA.
None:
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.
Related Concept Videos
Distributions to Estimate Population Parameter
Mechanistic Models: Compartment Models in Individual and Population Analysis
Analysis of Population Pharmacokinetic Data
Statistical Methods for Analyzing Epidemiological Data
Sample Proportion and Population Proportion
Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

