Epi Class 9 Flashcards Preview

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Flashcards in Epi Class 9 Deck (30):
1

Confounding

distorts or hides a relationship

When two exposures are related and that makes it look like exposure A causes the disease when really it is exposure B that causes the disease.

2

Effect modification

real effect is different in different populations

When different subpopulations with different biological responses are incorrectly grouped (means the RR or OR does not reflect the impact of effect modification)

3

No 3rd Variable Effect

OR1 = OR2 = ORcrude (all equal)

4

Types of Bias

Selection bias

Misclassification bias

Information bias

5

Selection bias

error due to systematic differences between those selected for study and those not selected for study

6

Misclassification bias

inaccuracies in methods of data acquisition may misclassify subjects

7

Information bias

bias in the way that information is collected from study participants (recall bias, interviewer bias, non-response bias)

8

Interaction

When the presence of two risk factors at the same time causes different outcomes than the presence of either one.

Can make both risk factors appear more risky or less risky than either really is.

Interaction can be additive or multiplicative

9

Confounding test steps

1. Confirm that the potential confounder is associated with the exposure

2. Confirm that the potential confounder is associated with the outcome

3. Calculate crude OR between exposure and outcome

4. Stratify by the potential confounder and calculate OR (or RR) for each stratum

10

Mantel-Haenszel (MH) analysis

creates a summary measure that is somewhere between OR1 and OR2 and adjusts for the sample size in each stratum

11

Effect Modification test steps

1. Confirm that the potential effect modifier is associated with the exposure

2. Confirm that the potential effect modifier is associated with the outcome

3. Calculate crude OR between exposure and outcome

4. Stratify by the potential effect modifier and calculate OR (or RR) for each stratum

12

The 3rd variable is an effect modifier if:

OR1 ≠ OR2 ≠ crude OR

13

The 3rd variable is a confounder if:

1. Stratified ORs are equal: OR1 = OR2
AND
2. Stratified ORs ≠ crude OR

14

Confounding Summary

Third variable effect:
distorts or hides a relationship

Evidence:
OR1 = OR2 ≠ ORcrude

Results of Breslow-Day test for homogeneity:
p > 0.05
- the strata (OR1 and OR2) are not different

Reporting: ORadjusted = ORmh
- use stratified analysis or multiple regression to find ORmh

15

Effect Modification Summary

Third variable effect:
Real effect is different in different populations

Evidence:
OR1 ≠ OR2 ≠ ORcrude (none equal)

Results of Breslow-Day test for homogeneity:
p < 0.05
- the strata (ORz and OR2) are different

Reporting:
Stratum-specific ORs: OR1 and OR2 listed separately

16

No Third Variable Effect Summary

Evidence:
OR1 = OR2 = ORcrude (all equal)

Results of Breslow-Day test for homogeneity:
p ≥ 0.05
- the strata (OR1 and OR2) are the same

Reporting:
ORcrude

17

4 Causal Relationships

1. Necessary and sufficient
2. Necessary but not sufficient
3. Sufficient but not necessary
4. Neither sufficient nor necessary

18

Necessary and Sufficient Causation

Rarely found

Example: exposure to an infectious agent causes disease in everyone with the exposure

19

Necessary but Not Sufficient Causation

Example: infectious agent must be contracted to get disease, but not everyone who is infected will become ill

Example: stages of carcinogenesis- each step is part of the causal pathway, but no one step is sufficient to cause cancer

20

Sufficient but Not Necessary Causation

Individual exposures are rarely sufficient, but multiple exposures could lead to disease

Example; radiation and benzene both can cause leukemia but are not necessary exposures

21

Neither Sufficient Nor Necessary Causation

Complex factors that contribute to chronic disease

22

Temporal relationship

exposure before disease.

(temporal = time)

23

Strength of association

OR, RR far from 1

24

Dose-response relationship

more exposure = higher risk of disease

25

Cessation of exposure

removing exposure reduces risk of disease

26

Specificity of the association

one exposure = one disease

27

How to reduce chance of bias

use large sample size to reduce chance of bias

28

Biologic plausibility

makes biological sense.

(shark/ice cream)

29

Replication of findings

other studies show the same thing

30

Consistency with other knowledge

Corresponds to other information published by the field