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Effect Modifiers

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Effect Modifiers

- To discuss the term interaction (effect modification)
- To discuss strategies for evaluation of interaction
- To discuss the detection of additive and multiplicative interactions
- To discuss when to use additive and multiplicative assessments
- To discuss the assessment of homogeneity of effects

- Two or more risk factors modify the effect of each other with regard to the occurrence or level of a given outcome
- Also known as effect modification
- Synergistic (positive interaction) – potentiates the effect of the exposure of interest
- Antagonistic (negative interaction) – diminishes or eliminates the effect of the exposure of interest

- Based on homo or heterogeneity of effects - effect of a putative risk factor A on the risk of an outcome Y is not homogeneous in strata formed by a third variable Z (the effect modifier)
- Base on comparisons between observed and expected joint effects - when the observed joint effect of A and Z differs from that expected on the basis of the independent effects of A and Z

- Additive – the AR in those exposed to Factor A varies as a function of a third variable.
- Multiplicative – when the relative difference (ratio) in the risk of an outcome Y between subject exposed and those not exposed to a putative risk factor A differs as a function of a third variable

- The absolute difference or attributable risk model (additive)
- The relative difference or Ratio model (multiplicative)

- AR is a measure of the association based on the absolute difference between two risk estimates
- Relative risk is the risk of developing the disease in the exposed compared to the unexposed
- Odds ratio is the ratio of the odds of developing the disease in the exposed compared to the unexposed

- In a case-control it is not possible to use additive methods, because incidence data is usually not available in this design
- Additive is used more for public health assessments and multiplicative is used more in prediction models
- Multiplicative is used more often because Mantel-Haenszel and multiple regression are based on multiplicative approach

- In case control can be used only to assess multiplicative interaction
- Interaction fallacy - odds ratios may be heterogeneous when relative risks are not. Most common in chronic disease epidemiology where risk of outcome is expected to be high or when there is a strong genetic susceptibility to the risk factor-induced disease.

10.0

- Age, CD4 and HIV mortality
- Race, CD4 and HIV mortality
- Are they confounders?
- Are they effect modifiers

Is there an additive effect?

Is there an additive effect?

Is there any evidence of multiplicative interaction?

Joint expected AR

(24.6 + 3.1) = 27.7

Joint observed AR

29.6

Is there any evidence

Of additive interaction?

Is there any evidence of multiplicative interaction?

Joint expected RR

(1.9 + 21.1) = 23.0

Joint observed RR

28.1

Is there any evidence

Of additive interaction?

- Sometimes the same variable may be both a confounder and an effect modifier
- Confounding makes it difficult to evaluate whether a statistical association is also causal
- Interaction is part of the web of causation
- Do not adjusted for a variable that is both a confounder and an effect modifer (reporting an average odds may be meaningless)