1. Causal Inference in Epi

Question 1

Definition of Epidemiology

Answer 1

The study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to control of health problems.

Question 2

What is the (1) scientific goal (2) public health goal (3) unifying theme, of epi

Answer 2

(1) understand causes of disease
(2) prevent and control disease
(3) understand cause is a prerequisite for effective prevention and control.

Question 3

Definition of cause

Answer 3

An antecedent event, condition, or characteristic that was necessary for the occurrence of the disease at the moment it occurred, given that other conditions are fixed.

Question 4

What is the fundamental challenge to determine causal inference?

Answer 4

1. We can’t observe cause, but only associations.
2. Whether it’s true pattern or just random or what we’ve picked out?

Question 5

How do epidemiologist thank about cause

Answer 5

1. Necessary cause
2. Sufficient cause

Question 6

What is a necessary cause?

Answer 6

No unexposed individuals ever become cases.

Smoking is not a necessary cause of lung cancer.

Question 7

What is a sufficient cause?

Answer 7

All exposed individuals inevitably become cases.

Smoking is not a sufficient cause of lung cancer.

Question 8

The Epidemiologic Triangle

Answer 8

Agent: exposure of interest

Host: affect susceptibility to disease

Environment: influence exposure and may affect susceptibility

Question 9

What does web of causation emphasize?

Answer 9

Importance of multiple causes of disease. Work for chronic conditions.

Question 10

Sufficient-Component Cause Model:

What is a sufficient cause

What is a component

Answer 10

–A set of minimal conditions and events acting jointly to form sufficient cause (disease).

–Components can be different for each disease.

–Sometimes there is 1 or more necessary component(s).

•Elimination of even 1 component may impact disease and important in prevention

Question 11

Counterfactual Model of Causation

(Potential Outcomes)

Answer 11

Based on the aspect of the definition of cause that had the cause been altered then the effect would have been different, then the counterfactual model stipulates that a contrast between outcomes of an individual under different exposure scenarios.

(a problem of non-identifiability arises when we do not know all exposures)

Question 12

Why we compare the group of individuals in a counterfactual model.

Answer 12

• Epidemiologically, our goal is to investigate average causal effects within populations
• Since we cannot compare an individual’s potential outcomes under different exposure scenarios, we compare groups of individuals with different exposure experiences, hoping that the one group is a good surrogate for the counterfactual (unobservable) experience

[The validity depends on the comparability of the distribution of the 4 types of individuals - no effect, effect causative, effect preventive , and no effect]

Question 13

What Makes a Good Scientific Hypothesis?

Answer 13

1. Testable: can be tested by experiments or further observation
2. Falsifiable: If predictions from a hypothesis are false then the hypothesis must also be false.
3. Parsimonious: intentionally simplified
4. Precise
5. Useful: Does the hypothesis advance our understanding of a phenomenon.

Question 14

Hill’s Guidelines (Criteria) for Causation

Answer 14

1. Strength of association
2. Consistency
3. Specificity
4. Temporality- (necessary, but it’s not sufficient)
5. Biological Gradient
6. Plausibility
7. Coherence
8. Experimental Evidence
9. Analogy

Question 15

SPECIFICITY

Answer 15

An association is limited to a specific exposure and disease.

PROBLEM: Inconsistent with what is known about a lot of diseases. Specificity postulated that a given agent is ALWAYS associated with only ONE disease and this agent can ALWAYS be found for that disease.

Question 16

COHERENCE

Answer 16

Interpretation of cause and effect should not conflict with what is known about the natural history and biology of disease.

PROBLEM: the distinction between coherence and biologic plausibility is very thin.

Question 17

ANALOGY

Answer 17

Similar associations exist between the health outcome of interest and other exposures.

Example: If a drug such as thalidomide causes birth defects then perhaps another drug can cause birth defects.

PROBLEM: However, little insight is gained through analogy.

Question 18

STRENGTH OF ASSOCIATION

Answer 18

The stronger an association the more likely it is to be causal.

PROBLEM: could be confounding.

Question 19

CONSISTENCY

Answer 19

Association is observed frequently, by different investigators, in different places, circumstances and times

Caution: could still mean that you see consistent confounding and bias. Could also be an artifact of publication bias.

Question 20

BIOLOGICAL GRADIENT

(exposure – Response)

Answer 20

As exposure increases, the frequency or rate of disease increases.

Question: If exposure-response does not exist, does this mean that causality does not exist?

Question 21

BIOLOGIC PLAUSIBILITY

Answer 21

Does an association coincide with what is known biologically?

PROBLEM: biologic plausibility is based on a priori evidence that may not stand the test of time

Question 22

EXPERIMENTAL EVIDENCE

Answer 22

Results from experimental studies should support the association.

PROBLEM: Experimental data are seldom available for human populations. Experimental evidence usually only exists from
animal studies.

Question 23

TEMPORALITY

Answer 23

For an exposure to be causal it must precede the event.