Effect modifiers
This presentation is the property of its rightful owner.
Sponsored Links
1 / 33

Effect Modifiers PowerPoint PPT Presentation


  • 358 Views
  • Uploaded on
  • Presentation posted in: Sports / Games

Effect Modifiers. Objectives. 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

Download Presentation

Effect Modifiers

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Effect modifiers

Effect Modifiers


Objectives

Objectives

  • 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


Interaction

Interaction

  • 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


Definition of interaction

Definition of Interaction

  • 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 versus multiplicative

Additive versus Multiplicative

  • 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


Strategies to evaluate interaction

Strategies to evaluate interaction

  • The absolute difference or attributable risk model (additive)

  • The relative difference or Ratio model (multiplicative)


Attributable risk versus relative risk

Attributable risk versus relative risk

  • 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


Additive versus multiplicative1

Additive versus Multiplicative

  • 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


Example of additive but not multiplicative interaction

Example of additive but not multiplicative interaction


Assessment of homogeneity of effects

Assessment of Homogeneity of effects

  • 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.


Assessing additive interaction

Assessing additive interaction


Assessing multiplicative interaction

Assessing multiplicative interaction

10.0


Examples

Examples

  • Age, CD4 and HIV mortality

  • Race, CD4 and HIV mortality

  • Are they confounders?

  • Are they effect modifiers


Example where multiplicative interaction is absent

Example where multiplicative interaction is absent

Is there an additive effect?


Example where multiplicative interaction is present

Example where multiplicative interaction is present

Is there an additive effect?


Mortality by age and cd4

Mortality by Age and CD4

Is there any evidence of multiplicative interaction?


Additive interaction the association of older age and lower cd4 counts on mortality

Additive Interaction – the association of older age and lower CD4 counts on Mortality

Joint expected AR

(24.6 + 3.1) = 27.7

Joint observed AR

29.6

Is there any evidence

Of additive interaction?


Mortality by race and cd4 count

Mortality by race and CD4 count

Is there any evidence of multiplicative interaction?


Multiplicative interaction the association of non white race and lower cd4 counts on mortality

Multiplicative Interaction – the association of Non-white race and lower CD4 counts on Mortality

Joint expected RR

(1.9 + 21.1) = 23.0

Joint observed RR

28.1

Is there any evidence

Of additive interaction?


Unadjusted and adjusted o r

Unadjusted and Adjusted O.R.


Confounding versus interaction

Confounding versus 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)


Risk factors for sinusitis among hiv infected persons in multivariate logistic regression

Risk factors for sinusitis among HIV-infected persons in Multivariate logistic regression


  • Login