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Partitioning the population attributable fraction for a sequential chain of effects.

Mason CA, Tu S - Epidemiol Perspect Innov (2008)

Bottom Line: While the population attributable fraction (PAF) provides potentially valuable information regarding the community-level effect of risk factors, significant limitations exist with current strategies for estimating a PAF in multiple risk factor models.These strategies can result in paradoxical or ambiguous measures of effect, or require unrealistic assumptions regarding variables in the model.In addition, the approach can also be used to statistically control for confounding by other variables, while avoiding the potential pitfalls of attempting to separately differentiate direct and indirect effects.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Education and Human Development, University of Maine, Orono, ME, USA. craig.mason@umit.maine.edu

ABSTRACT

Background: While the population attributable fraction (PAF) provides potentially valuable information regarding the community-level effect of risk factors, significant limitations exist with current strategies for estimating a PAF in multiple risk factor models. These strategies can result in paradoxical or ambiguous measures of effect, or require unrealistic assumptions regarding variables in the model. A method is proposed in which an overall or total PAF across multiple risk factors is partitioned into components based upon a sequential ordering of effects. This method is applied to several hypothetical data sets in order to demonstrate its application and interpretation in diverse analytic situations.

Results: The proposed method is demonstrated to provide clear and interpretable measures of effect, even when risk factors are related/correlated and/or when risk factors interact. Furthermore, this strategy not only addresses, but also quantifies issues raised by other researchers who have noted the potential impact of population-shifts on population-level effects in multiple risk factor models.

Conclusion: Combined with simple, unadjusted PAF estimates and an aggregate PAF based on all risk factors under consideration, the sequentially partitioned PAF provides valuable additional information regarding the process through which population rates of a disorder may be impacted. In addition, the approach can also be used to statistically control for confounding by other variables, while avoiding the potential pitfalls of attempting to separately differentiate direct and indirect effects.

No MeSH data available.


Calculating PAFAGG.
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Figure 2: Calculating PAFAGG.

Mentions: The most transparent approach for estimating a PAF across multiple risk factors is to use a stratified model. In a stratified model, the sample is stratified based upon the possible combinations of risk factors, and a PAF is estimated for each combination. The referent group is those without any of the risk factors under consideration. An example of this approach is presented in Figure 2, where risk factor A and risk factor B are risk factors for MMR in children. The referent group consists of children with neither risk factor, and there are three "at-risk" groups: those with A only, those with B only, and those with both A and B. A PAF is calculated for any one these combinations of A and/or B using Equation 1.


Partitioning the population attributable fraction for a sequential chain of effects.

Mason CA, Tu S - Epidemiol Perspect Innov (2008)

Calculating PAFAGG.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2572052&req=5

Figure 2: Calculating PAFAGG.
Mentions: The most transparent approach for estimating a PAF across multiple risk factors is to use a stratified model. In a stratified model, the sample is stratified based upon the possible combinations of risk factors, and a PAF is estimated for each combination. The referent group is those without any of the risk factors under consideration. An example of this approach is presented in Figure 2, where risk factor A and risk factor B are risk factors for MMR in children. The referent group consists of children with neither risk factor, and there are three "at-risk" groups: those with A only, those with B only, and those with both A and B. A PAF is calculated for any one these combinations of A and/or B using Equation 1.

Bottom Line: While the population attributable fraction (PAF) provides potentially valuable information regarding the community-level effect of risk factors, significant limitations exist with current strategies for estimating a PAF in multiple risk factor models.These strategies can result in paradoxical or ambiguous measures of effect, or require unrealistic assumptions regarding variables in the model.In addition, the approach can also be used to statistically control for confounding by other variables, while avoiding the potential pitfalls of attempting to separately differentiate direct and indirect effects.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Education and Human Development, University of Maine, Orono, ME, USA. craig.mason@umit.maine.edu

ABSTRACT

Background: While the population attributable fraction (PAF) provides potentially valuable information regarding the community-level effect of risk factors, significant limitations exist with current strategies for estimating a PAF in multiple risk factor models. These strategies can result in paradoxical or ambiguous measures of effect, or require unrealistic assumptions regarding variables in the model. A method is proposed in which an overall or total PAF across multiple risk factors is partitioned into components based upon a sequential ordering of effects. This method is applied to several hypothetical data sets in order to demonstrate its application and interpretation in diverse analytic situations.

Results: The proposed method is demonstrated to provide clear and interpretable measures of effect, even when risk factors are related/correlated and/or when risk factors interact. Furthermore, this strategy not only addresses, but also quantifies issues raised by other researchers who have noted the potential impact of population-shifts on population-level effects in multiple risk factor models.

Conclusion: Combined with simple, unadjusted PAF estimates and an aggregate PAF based on all risk factors under consideration, the sequentially partitioned PAF provides valuable additional information regarding the process through which population rates of a disorder may be impacted. In addition, the approach can also be used to statistically control for confounding by other variables, while avoiding the potential pitfalls of attempting to separately differentiate direct and indirect effects.

No MeSH data available.