A Comparative Study of Parametric and Nonparametric Estimates of the Attributable Fraction for a Semi-continuous Exposure

Wang, Wei; Small, Dylan S.

doi:10.1515/1557-4679.1389

Cited by 5 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The branches of robust and nonparametric statistics deal with distribution selection problems. Nonparametric methods to estimate attributable fractions in cohort and case-control studies have been proposed (Wang and Small, 2012;Hanley, 2001;Chen et al, 2010;Sjölander and Vansteelandt, 2017;Taguri et al, 2012). In particular, Sjölander (2011); Taguri et al…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Nonparametric Estimation of the Potential Impact Fraction and Population Attributable Fraction with Individual-Level and Aggregated Data

Chan¹,

Zepeda-Tello²,

Camacho-García-Formentí³

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

“…where E obs X RR(X; β) represents the expected value of the relative risk under the observed exposure distribution in a given population and E cft X RR(X; β) is the expected value of the relative risk under a counterfactual distribution of the exposure (Taguri et al, 2012;Wang and Small, 2012).…”

Section: Potential Impact Fractionmentioning

confidence: 99%

Nonparametric Estimation of the Potential Impact Fraction and Population Attributable Fraction with Individual-Level and Aggregated Data

Chan¹,

Zepeda-Tello²,

Camacho-García-Formentí³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…where is an indicator function [ 18 ]. Justification of the convergence of the estimate ( 6 ) to the PAF in ( 5 ) is provided by [ 6 ] in the scenario of no other covariates. A similar proof adjusting for the X can also be derived.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, the definition of the PAF itself also suggests a monotone relationship between the disease risk and the exposure level (a justification is presented at the end of the next section). Incorporating the monotonicity assumption into the estimation of the PAF provides performance gains when there are no other covariates [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Semiparametric estimation of the attributable fraction when there are interactions under monotonicity constraints

Wang

Small

Harhay

2020

BMC Med Res Methodol

Self Cite

View full text Add to dashboard Cite

Background The population attributable fraction (PAF) is the fraction of disease cases in a sample that can be attributed to an exposure. Estimating the PAF often involves the estimation of the probability of having the disease given the exposure while adjusting for confounders. In many settings, the exposure can interact with confounders. Additionally, the exposure may have a monotone effect on the probability of having the disease, and this effect is not necessarily linear. Methods We develop a semiparametric approach for estimating the probability of having the disease and, consequently, for estimating the PAF, controlling for the interaction between the exposure and a confounder. We use a tensor product of univariate B-splines to model the interaction under the monotonicity constraint. The model fitting procedure is formulated as a quadratic programming problem, and, thus, can be easily solved using standard optimization packages. We conduct simulations to compare the performance of the developed approach with the conventional B-splines approach without the monotonicity constraint, and with the logistic regression approach. To illustrate our method, we estimate the PAF of hopelessness and depression for suicidal ideation among elderly depressed patients. Results The proposed estimator exhibited better performance than the other two approaches in the simulation settings we tried. The estimated PAF attributable to hopelessness is 67.99% with 95% confidence interval: 42.10% to 97.42%, and is 22.36% with 95% confidence interval: 12.77% to 56.49% due to depression. Conclusions The developed approach is easy to implement and supports flexible modeling of possible non-linear relationships between a disease and an exposure of interest.

show abstract

“…A slightly more complicated model that was preferred by Smith et al (1994) adds an additional power parameter to the logistic regression. Also, nonparametric regression models can be considered such as local linear smoothing (Wang & Small 2012). However, if data are collected from a case-control study and the prevalence of fever is not known, then P (Y obs = 1|D) cannot be fully estimated, but p f and p a can be estimated.…”

Section: Additional Assumptions Needed For Existing Estimatorsmentioning

confidence: 99%

Estimating the Malaria Attributable Fever Fraction Accounting for Parasites Being Killed by Fever and Measurement Error

Lee

Small

2019

Journal of the American Statistical Association

View full text Add to dashboard Cite

Malaria is a parasitic disease that is a major health problem in many tropical regions. The most characteristic symptom of malaria is fever. The fraction of fevers that are attributable to malaria, the malaria attributable fever fraction (MAFF), is an important public health measure for assessing the effect of malaria control programs and other purposes. Estimating the MAFF is not straightforward because there is no gold standard diagnosis of a malaria attributable fever; an individual can have malaria parasites in her blood and a fever, but the individual may have developed partial immunity that allows her to tolerate the parasites and the fever is being caused by another infection. We define the MAFF using the potential outcome framework for causal inference and show what assumptions underlie current estimation methods. Current estimation methods rely on an assumption that the parasite density is correctly measured. However, this assumption does not generally hold because (i) fever kills some parasites and (ii) the measurement of parasite density has measurement error. In the presence of these problems, we show current estimation methods do not perform well. We propose a novel maximum likelihood estimation method based on exponential family g-modeling. Under the assumption that the measurement error mechanism and the magnitude of the fever killing effect are known, we show that our proposed method provides approximately unbiased estimates of the MAFF in simulation studies. A sensitivity analysis can be used to assess the impact of different magnitudes of fever killing and different measurement error mechanisms. We apply our proposed method to estimate the MAFF in Kilombero, Tanzania. ). The Authors thank Thomas Smith for insightful suggestions and providing the data.

show abstract

A Comparative Study of Parametric and Nonparametric Estimates of the Attributable Fraction for a Semi-continuous Exposure

Cited by 5 publications

References 34 publications

Nonparametric Estimation of the Potential Impact Fraction and Population Attributable Fraction with Individual-Level and Aggregated Data

Nonparametric Estimation of the Potential Impact Fraction and Population Attributable Fraction with Individual-Level and Aggregated Data

Semiparametric estimation of the attributable fraction when there are interactions under monotonicity constraints

Estimating the Malaria Attributable Fever Fraction Accounting for Parasites Being Killed by Fever and Measurement Error

Contact Info

Product

Resources

About