This document discusses key concepts in epidemiology for comparing disease frequency between groups with different exposures. It explains that epidemiological studies measure associations between exposures and outcomes, but not necessarily causal effects. An association indicates how likely past events were, while a causal effect shows how probabilities change if exposures change. Studies aim to minimize bias and chance error to help infer causality from significant associations.

1. Big picture
• In epidemiology, we often compare measures of
disease frequency between different groups
– Building on measures of disease module
• Typically comparing disease frequency between
groups with different exposures
• The goal is to ascertain associations between
exposures and outcomes and, ultimately, effects
of exposures on outcomes

2. Big picture
• Reminder on the distinction between
associations and effects
• An association tells us about probabilities of past
events
– Carrying matches is associated with lung cancer

3. Big picture
• An effect is causal and it tells us how
probabilities change if conditions change
– If you remove matches from pockets in the
population, does the rate of lung cancer decrease?
?

4. Big picture
• Measures of association quantify the strength
and direction of associations between exposures
and outcomes
• Strength of association:
– Is exposure weakly related to outcome? Strongly?

5. Big picture
• Direction of association:
– Does exposure increase risk of disease (risk factor)?
– Does exposure decrease risk (protective factor)?
– Is there no association (null result)?

6. Big picture
• Recall counterfactual framework illustrated by an
“ideal experiment”
• A hypothetical study which, if we could actually
conduct it, would allow us to infer causality
– Population experiences one exposure and observed
for outcome over a given time period
– Roll back the clock
– Change the exposure but leave everything else the
same, observe for outcome over the same time period
– Compare the outcomes under both exposures: this is
the causal effect

7. Big picture
• In reality, the “ideal experiment” cannot be conducted
• We evaluate the associations between exposures and
outcomes by comparing outcomes between groups that
experienced different exposures

8. Big picture
• Measure of association does not necessarily (or
even typically) quantify a causal effect
• Associations may be explained by various
sources:
– Causal relation
– Chance (random error)
– Bias (systematic error)

9. Big picture
• Throughout course will enumerate the methods
for:
– Designing studies to minimize error (systematic and
random)
– Assessing the roles of systematic and random error
once a study is conducted (qualitatively and
quantitatively)
– Incorporating knowledge or assumptions about the
causal process
– Analytically removing or adjusting for error to the
extent possible

10. Big picture
• These are key steps to moving towards inferring
causality from associations