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Unit 7: Effect Measure Modification And Intervention Studies. Unit 7 Learning Objectives: Understand the concept of “effect measure modification”. Employ methods to investigate effect measure modification on both additive and multiplicative scales.
Effect Measure Modification
And Intervention Studies
Chapter 15, pages 233-238 (Interaction)
Chapter 7, Randomized Trials
Chapter 8, Randomized Trials: some further issues
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. New England Journal of Medicine 2002; 346:393-403.
Effect Measure Modification: The magnitude or direction of an association varies according to levels of a third factor.
• “Effect Modification”
Note: Unlike confounding, effect measure modification should be described and reported, rather than controlled.
Hypothesis:High alcohol consumption is associated with larynx cancer (cohort study)
RR = (30 / 200) / (15 / 315)
RR = 3.15
•Persons with high alcohol consumption appear to be at 3.15 times higher risk of developing larynx cancer than persons without high alcohol consumption. However, is this elevated risk similar among smokers and non-smokers?
RR = (4 / 53) / (6 / 156)
RR = 1.96
RR = (26 / 147) / (9 / 159)
RR = 3.12
Does smoking modify the relationship between
alcohol consumption and larynx cancer?
RRCA = 3.15
RRNS = 1.96
RRSM = 3.12
Unlike the assessment of confounding, the crude
estimate is NOT USED to evaluate the presence of
effect measure modification.
Instead, the stratum-specific estimates are
compared directly to see if they are different
This example suggests “risk-ratio heterogeneity.”
Keep in mind that the presence of “effect
measure modification” depends on which
measure of effect is evaluated (e.g. risk
difference, risk ratio, etc.).
The RD is on an additive scale.
The RR is on a multiplicative scale.
Let’s look at RD and RR separately.
Expected additive = (0.075 + 0.057) – 0.038 = 0.094
Expected multiplicative = 1.96 x 1.47 = 2.89
In this example, it appears that smoking modifies
(increases) both the risk difference and risk ratio
between alcohol consumption and larynx cancer.
Hypothesis:Female gender is associated with depression (cohort study)
RR = (100 / 280) / (18 / 233)
RR = 4.62
• Females appear to be at 4.62 times higher risk of depression than males. However, is this elevated risk similar among young persons and older persons?
RD = (6 / 54) - (6 / 150)
RD = 0.111 – 0.040 = 0.071
RD = (94 / 226) - (12 / 83)
RD = 0.416 – 0.145 = 0.271
RR = (6 / 54) / (6 / 150)
RR = 0.111 / 0.040 = 2.78
RR = (94 / 226) / (12 / 83)
RR = 0.416 / 0.145 = 2.88
Expected additive = (0.111 + 0.145) – 0.040 = 0.216
Expected multiplicative = 2.78 x 3.61 = 10.04
In this example, older age modifies (increases) the
risk difference between gender and depression.
However, the risk ratio is not modified by older age (no risk ratio heterogeneity).
Hypothesis:Depression is associated with risk of hip fracture (cohort study)
RR = (40 / 220) / (30 / 245)
RR = 1.48
•Depressed persons appear to be at 1.48 times higher risk of hip fracture than non-depressed persons. However, is this elevated risk similar among persons with low and high body mass index (BMI)?
RD = (6 / 56) - (6 / 150)
RD = 0.107 – 0.040 = 0.067
RD = (34 / 164) - (24 / 95)
RD = 0.207 – 0.253 = -0.045
RR = (6 / 56) / (6 / 150)
RR = 0.107 / 0.040 = 2.68
RR = (34 / 164) / (24 / 95)
RR = 0.207 / 0.253 = 0.82
Expected additive = (0.107 + 0.253) – 0.040 = 0.320
Expected multiplicative = 2.68 x 6.32 = 16.92
In this example, it appears that high BMI modifies
(decreases) both the risk difference and risk ratio
between depression and risk of hip fracture.
1. The presence of effect measure modification
should be assessed by “eyeballing” the stratum-specific estimates to see if they differ.
2. Unlike confounding, which is a nuisance effect,
effect measure modification represents useful
information that should be explored and reported.
3. In the presence of effect measure modification,
calculation and reporting of an overall (crude) effect is of dubious value, and is potentially misleading.
4. Statistical tests of homogeneity of the stratum-
specific estimates can be performed, but these tests are often underpowered – “eyeballing” the stratum-specific estimates is a better approach.
5. Be careful in the number of subgroups in which
effect measure modification is investigated – each additional investigation increases the likelihood of a type I error (chance finding in which the null hypothesis is erroneously rejected).
6. Although some authors define effect measure
modification (interaction) as any effect greater than additive, this is inappropriate since the stratum-specific estimates can differ in a non-additive fashion.
7. Any third variable has the potential to be:
a) Confounder and effect modifier
b) Confounder and not an effect modifier
c) Not a confounder and an effect modifier
Thus, there is no relationship between confounding and effect measure modification.