Lecture 11: Bias Part 2

Question 1

What is the difference between association and causation?

Answer 1

Association:
-Occurs when theres an identifiable relationship between an E and O
-Implies that E might cause disease

Causation
-Implies that there is a true mechanism that leads from E to O (something that ensures lung cancer will happen)

Question 2

How do we determine causality?

Answer 2

3 Ways:

1. Statistical association
2. Epidemiological association
3. Casual inference/understanding
   (can also relate to each other not independent)

Question 3

What is a statistical association?

Answer 3

-Associations: 2 events must occur together more or less frequently than would be expected due to chance alone
-Formal statistical tests only test whether chance could produce observed difference (important but not everything need to consider systemic bias ie ones last lecture)
-Can use P value = statistically significant
if p< 0.05 then sat sig and can reject the null
if p>0.05 then not sat sig and therefore dont have evidence to reject the null

Question 4

What are the Epi measures of association

Answer 4

-Relationship b/w and exposure and its outcome (odds ratios, risk ratios)
-The stronger the relationship & bigger numerical value of measure of association the more evidence it provides that it could potentially be causal
-ASK: does a significant and strong associated exists b/w the exposure and the outcome?

Question 5

What is a causal inference: Bradford- Hill criteria?

Answer 5

1. Temporal sequence (most important) where E precedes O
2. Strength of Association (Risk ratio RR or odds ratio OR very high or very low–> less likely to be due to chance or bias)
3. Dose-response (risk of disease increases directly wit the level of exposure)
   -Changes in E are related to a trend in association
4. Consistency/Replication of findings
   -Multiple studies with the same results (different populations, different circumstances, different study designs)
5. Coherence/biological plausibility
   -But : new findings/changing knowledge
   -Also consider that we sometimes know little about the underlying biology( remember the black box)
6. Not as relevant: Specificity
   -Easier to support causation when the associations are specific –> but often multiple effects per cause and vice vera ex smoking
7. not as relevant: Analogy
   -similar results in other species
   -But may be specific to a particular species
8. Experimental (best if done with target species as close to ‘natural conditions’ as possible

Question 6

What are the “additions” to Hills Criteria?

Answer 6

-Consideration of alternate explanations (ex cofounding, anything else that can explain this)
-Cessation of exposure
-If factor is a cause of disease, expect risk of disease to decline when reduce exposure but in some cases, pathogenic process may be too far gone or not reversible
-Consistency with other knowledge

Question 7

What is the causal inference: Component-Cause Model?

Answer 7

-Allows us to take what er have learned from the stats and epi associations and try to infer causation

Necessary cause
-Precedes the disease
-If not present, disease can’t occur
-It will always be present if the disease is present

Sufficient cause
-Precedes the disease
-If present the disease always occurs

Question 8

What are component causes?

Answer 8

-Multifactorial is broken down into component causes, which are individual factors that can be combined to create a single sufficient cause
-One of a number of factors that in combination, constitute a sufficient cause
-Removing one component of a sufficient cause will render in insufficient

ex sufficient cause of heart attacks
1. Smoking
2. Hypertension
3. Obesity
4.Lack of exercise

Question 9

What is the importance of causal factors?

Answer 9

-Epidemiologist & public health are often interested in how much disease could be completely removed from the pop by removing the exposure of interest (pop attribute fraction)
-Population attribute fraction: if the proportion of disease in the pop that is attributable to the exposure (and thus could be eliminated if the exposure was eliminated)
-Can add up to more than 100% bc some components are involved in more than 1 sufficient cause, means we double count the role in each sufficient cause

Question 10

What is a recap of component-casue model?

Answer 10

Necessary cause: Precedes the disease/outcome and MUST be present for the disease to occur (ie its NECESSARY) ex infectious (covid or influenza)

Sufficient cause: Precedes the disease/outcome and ALWAYS produces the disease/outcome typically multifactorial ex radiation exposure in thyroid occurs, radiation is only 1 way to get cancer, but not everyone who has radiation will get cancer

Component cause: One of a number of factors that, in combination, constitutes a sufficient cause

Question 11

What is the web of causation?

Answer 11

-no single cause of disease- often an interplay of many factors
-Causes are interacting
-Causal webs help illustrate the inter-connectedness of possible casues

Question 12

What is the bottom line of this lecture?

Answer 12

-Associations dont necessarily = causation –> need to assess study design, statistics, epidemiological associations etc in light of criteria for causation
(various criteria/models of causation can help)
-using bradford-hill criteria, component-casue model, web of causation