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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.


Two unrelated risk factors with an interaction.
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Figure 5: Two unrelated risk factors with an interaction.

Mentions: These next two examples illustrate how sequential partitioning is applied to data in which the risk factors have an interaction effect. We will first consider an interaction in a stratified analysis with risk factors that are unrelated/uncorrelated with each other. For example, there may be no association between child gender and maternal smoking (i.e. child gender has no indirect effect on the outcome through maternal smoking); however, being both male and having a mother who smoked may result in greater risk than would be expected given the individual risks of being male and maternal smoking, alone. As noted previously, a stratified analysis would be acceptable given the risk factors are not related to each other. Figure 5 contains hypothetical data for which an interaction exists between two unrelated risk factors, A and B, as predictors of MMR. Assuming no interaction, the expected risk ratio among individuals with both risk factors is equal to...


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

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

Two unrelated risk factors with an interaction.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Two unrelated risk factors with an interaction.
Mentions: These next two examples illustrate how sequential partitioning is applied to data in which the risk factors have an interaction effect. We will first consider an interaction in a stratified analysis with risk factors that are unrelated/uncorrelated with each other. For example, there may be no association between child gender and maternal smoking (i.e. child gender has no indirect effect on the outcome through maternal smoking); however, being both male and having a mother who smoked may result in greater risk than would be expected given the individual risks of being male and maternal smoking, alone. As noted previously, a stratified analysis would be acceptable given the risk factors are not related to each other. Figure 5 contains hypothetical data for which an interaction exists between two unrelated risk factors, A and B, as predictors of MMR. Assuming no interaction, the expected risk ratio among individuals with both risk factors is equal to...

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.