Introduction to Epidemiology: Revision Guide
This revision guide has been designed to help revise the basic concepts of the Intro to Epi course.
You are welcome to use it to guide your revision process
Types of study design
Study Design
RCT
Case-control
Cohort Study
Ecological Study
Cross-sectional
Study
Details
A RCT recruits
participants who are
then assigned to a
control group or an
intervention group.
The outcome is
observed from both
groups after the
intervention has been
completed.
A case-control study
is a study where
researchers observe
cases, find non-cases,
and then assess
previous exposures
for both groups.
A cohort study is used
when assessing
population outcomes.
Participants are
chosen as per predetermined
characteristics, such
as birth year.
An ecological study is
focused on multiple
populations. There
are no individual
participants, but
comparison is made
between entire
populations.
A cross sectional
study sets to examine
the prevalence of an
outcome at a specific
time. Measurements
are taken to create a
‘snapshot’ of the data
at one point.
Example
We want to assess
whether an antismoking campaign is
effective at changing
smoking behaviours
in individuals.
Advantages/disadvantages
Good:
o Exchangeability on
potential outcomes
o Highly trained staff
o Strictly controlled
Bad:
o Not representative
o Difficult to recruit
o Expensive
Out of 50 people who
have lung cancer, we
want to assess how
many of them were
smokers and how
many weren’t. Is
smoking an exposure
for lung cancer?
We want to assess
the impact of antismoking messages
among different age
groups. Participants
are characterised by
birth year.
Good:
o Cheap and fast
o Good for rare disease
o Can calculate RR and IRR
Bad:
o Selection bias
o Recall bias
o Confounding bias
Good:
o Many measures
o Reduce recall bias
o Show order (XY)
Bad:
o Large sample sizes
o Expensive and long
When looking at the
effect of smoking on a
global scale, we want
to assess the
prevalence of
smoking across
populations and their
respective death
statistics.
To evaluate whether
an anti-smoking
campaign was
effective, data
regarding smoking
behaviour is collected
before and after the
intervention to
compare.
Good:
o Large-scale comparisons
o Monitors global health
o Hypothesis generating
o Cheap and rapid (if data
is available)
Bad:
o Ecological fallacy
1
Good:
o Good for large samples
o Prevalence of disease
measure
o Many variables
Bad:
o Does not show order
o Recall bias
Be very clear on what exposure, outcome and a confounder is….
Confounder
Outcome
Exposure
Potential sources of bias (this list is from the Webb & Bain Textbook, which you can access online)
Name of bias
Definition
Example/Detail
Confounding
When the relationship of
Occurs 1st – Characteristic of
interest is affected by another the real world because
factor.
humans live ‘correlated lives’
and behaviours cluster.
Selection bias
Inappropriate selection of
Occurs 2nd – Not a
study and comparison groups
characteristic of the real world,
due to different criteria or
but a characteristic of how
systemic differences.
studies are designed and how
the population is sampled.
Information bias
Occurs due to error in the
Occurs 3rd – Not a
measurement of the outcome
characteristic of the real world
or misclassification of
and is created by how things
participants with respect to
are measured.
their exposure status or
outcome.
List of Calculations
Case
Non-case
Total
Exposed
a
b
a+b
Unexposed
c
d
c+d
Total
a+c
b+d
a+b+c+d
Disease (D+)
No disease (D-)
Positive test (T+)
T+D+
T+D-
Negative test (T-)
T-D+
T-D-
Name
Prevalence
Incidence
Notes/Formula
Cases/Total defined
population
New cases/Total
defined population
2
Example
a/a+b+c+d
-
Risk difference
Prevalence Ratio
Risk ratio (aka. relative risk)
Odds ratio
Sensitivity
Specificity
Positive Predictive Value
Negative Predictive Value
Outcome among
exposed – outcome
among unexposed
Prevalence among
exposed/prevalence
among unexposed
Incidence among
exposed/incidence
among unexposed
Odds of disease in
exposed/odds of
disease in unexposed
# of positive tests/# of
cases
# of negative tests/# of
non-cases
# of cases/# of positive
tests
# of non-cases/# of
negative tests
3
(a/a+b) – (c/c+d)
a/c
(a/a+b)/(c/c+d)
(a/b)/(c/d)
T+/D+
T-/DD+/T+
D-/T-