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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.

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slide1

Unit 7:

Effect Measure Modification

And Intervention Studies

slide2

Unit 7 Learning Objectives:

  • Understand the concept of “effect measure modification”.
  • Employ methods to investigate effect measure modification on both additive and multiplicative scales.
  • Recognize the differences between observational and experimental studies.
  • Distinguish between therapeutic and preventive intervention studies.
  • Understand design features of randomized clinical trials.
slide3

Unit 7 Learning Objectives:

  • Recognize ethical issues in clinical trials.
  • Recognize the role of Institutional Review Boards in clinical trials.
  • Understand the use of random allocation, factorial designs, and cross-over designs in experimental epidemiology.
  • Understand the use of blinding (masking) in the conduct of experimental studies.
  • Understand the impact of non-participation, compliance, and attrition of subjects in experimental studies.
slide4

Assigned Readings:

Textbook (Gordis):

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.

slide5

Introduction to Effect

Measure Modification

slide6

Effect Measure Modification

Effect Measure Modification: The magnitude or direction of an association varies according to levels of a third factor.

Also called:

• “Effect Modification”

• “Interaction”

Note: Unlike confounding, effect measure modification should be described and reported, rather than controlled.

slide7

Effect Measure Modification

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?

slide8

Effect Measure Modification

NON-SMOKERS

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?

slide9

Effect Measure Modification

CRUDE

RRCA = 3.15

STRATA 1

RRNS = 1.96

STRATA 2

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

(heterogeneous).

This example suggests “risk-ratio heterogeneity.”

slide10

Effect Measure Modification

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.

slide11

Effect Measure Modification

Expected additive = (0.075 + 0.057) – 0.038 = 0.094

Expected multiplicative = 1.96 x 1.47 = 2.89

slide12

Effect Measure Modification

In this example, it appears that smoking modifies

(increases) both the risk difference and risk ratio

between alcohol consumption and larynx cancer.

slide13

Effect Measure Modification

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?

slide14

Effect Measure Modification

YOUNG

OLD

RD =

RD =

RR =

RR =

slide15

Effect Measure Modification

YOUNG

OLD

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

slide17

Effect Measure Modification

Expected additive =

Expected multiplicative =

slide18

Effect Measure Modification

Expected additive = (0.111 + 0.145) – 0.040 = 0.216

Expected multiplicative = 2.78 x 3.61 = 10.04

slide19

Effect Measure Modification

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).

slide20

Effect Measure Modification

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)?

slide21

Effect Measure Modification

LOW BMI

HIGH BMI

RD =

RD =

RR =

RR =

slide22

Effect Measure Modification

LOW BMI

HIGH 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

slide24

Effect Measure Modification

Expected additive =

Expected multiplicative =

slide25

Effect Measure Modification

Expected additive = (0.107 + 0.253) – 0.040 = 0.320

Expected multiplicative = 2.68 x 6.32 = 16.92

slide26

Effect Measure Modification

In this example, it appears that high BMI modifies

(decreases) both the risk difference and risk ratio

between depression and risk of hip fracture.

slide27

Effect Measure Modification

Axioms:

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.

slide28

Effect Measure Modification

Axioms:

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).

slide29

Effect Measure Modification

Axioms:

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.

observational studies
Observational Studies
  • Investigator observes the natural course of events.
    • Documents who is exposed or non- exposed
    • Documents who has or has not developed the outcome of interest
experimental intervention studies
Experimental (Intervention) Studies
  • Investigator allocates the exposure
      • Therapeutic (Secondary Prevention)
      • Prevention (Primary Prevention)
  • Follow subjects to document subsequent development of disease
experimental intervention studies1
Experimental (Intervention) Studies
  • Therapeutic Trials - almost always conducted among individuals (e.g. clinical trial)
  • Prevention Studies - may be conducted among individuals (e.g. field trial) or among entire populations (community trial)
therapeutic clinical trial
Therapeutic Clinical Trial
  • Participants have a disease or condition
  • Therapies are tested for safety and effectiveness (secondary prevention)
preventive field trial
Preventive Field Trial
  • Participants (individuals) are free from the condition of interest
  • Potential preventive treatments are tested -- can include healthy individuals at usual risk, or persons recognized to be at high risk (primary prevention)
preventive community trial
Preventive Community Trial
  • Entire communities are randomly allocated to treatments of interest
  • Example: Newburgh-Kingston dental caries study