Introduction to epidemiological
study design
Study = basic tool in epidemiology
Epidemiology = comparison
 550 cases of stomach cancer
Epidemiology = comparison
 550 cases of stomach cancer in
Hertfordshire in 2005
Epidemiology = comparison
 550 cases of stomach cancer in
Hertfordshire in 2005
 Population 550,000
 Rate 100/100,000
Stomach cancer by age group, 2005, per
100,000
0
50
100
150
200
250
300
350
400
450
500
<25 25-34 35-44 45-54 55-64 65-74 75+
Stomach cancer in Hertfordshire,
1950-2005, per 100,000
0
50
100
150
200
250
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Stomach cancer in SE England in 2005, per
100,000
0
20
40
60
80
100
120
140
160
180
G
reaterLondonInnerLondon
Essex
SussexHertfordshire
M
iddlesex
Kent
Epidemiology = comparison
 Type of comparison (= type of study)
depends on purpose.
 E.g.
◦ Describe the disease / condition
◦ Study (analyse) its determinants / causes
◦ Study (analyse) prevention / treatment
Two primary criteria
 Descriptive vs. analytical
 Observational vs. interventional
Descriptive vs. analytical studies
 describe a pattern of occurrence of a
disease: descriptive studies (always
observational).
 to analyse the relationship between a
disease and an exposure of interest:
analytical studies (can be both
observational and interventional)
Descriptive studies
 Describe patterns of disease occurrence
 Useful for:
◦ health services planning
◦ hypothesis formulation in research
 Usually based on existing data:
◦ mortality
◦ reporting of diseases (infections, STDs, cancers...)
◦ hospital and medical records
◦ Census
Descriptive studies
4 Ws :What? Who?Where? When?
What? health outcome / case / event
Person (Who?)
Age, sex, ….
Place (Where?)
Regions, countries, international comparisons
Time (When?)
When events occurred:
● specific time period
● seasonal pattern (births, deaths, infections)
Analytical studies
Analytical
studies
Observational
Ecological
Cross-
sectional
Cohort Case-control
Interventional
Randomised
control trial
Community
interventions
Population based Individual based Individual based Population based
Cross-sectional studies
Cross-sectional studies
 In a cross-sectional study, all information is
collected at one point in time
◦ Outcome
◦ Exposures
◦ Covariates
 Sometimes called “survey”
 Cross-sectional studies could be
descriptive or analytical
 Always observational
 The unit of analysis is the individual
Cross-sectional study
Time
Survey – all measurements
The only way to measure “exposures”
and “outcomes” is
- at the time of survey or
- retrospectively
Cross-sectional studies:Advantages
 Relatively quick, do not require follow up
 Provide a snapshot, e.g. prevalence of a
disease or a risk factor in population
 Allow examination of multiple diseases and
multiple exposures
 Can test or suggest hypotheses
Cross-sectional studies: Limitations
 Since both disease and exposures are measured at the
same time, temporality is unclear
 Difficult to estimate past exposure, especially if it
occurred long time ago. Not ideal for studying
exposures that change over time (e.g. diet). (but no
problem with factors that are stable over time, e.g.
genetic markers.)
 Sensitive to reporting or recall bias if exposures are
subjectively reported.
 Sensitive to response rates and representativeness
if used to estimate prevalence of a condition in
population.
Ecological studies
Ecological studies
 The unit of analysis is a group (e.g.
country, district, population etc)
 Data cannot be disaggregated to the level
of an individual.
 Also sometimes called correlation studies or
geographical studies
 Include comparisons over time (time-
series)
 Usually cheap and quick
Fish consumption and mortality
Ecological fallacy
 This is a logical fallacy in the interpretation of
statistical data where inferences about the
nature of individuals are deduced from
inference for the group to which those
individuals belong
 Extrapolation from groups to individuals
is conceptually inappropriate
 Situation when individual-level and group-level
(ecological) associations differ
 Individual data are necessary to estimate the
association at the level of the individual
Ecological fallacy (1)
Blood pressure
Salt intake
Ecological fallacy (2)
Blood pressure
Salt intake
Ecological fallacy (3)
Blood pressure
Salt intake
Ecological fallacy (4)
Blood pressure
Salt intake
Example:The INTERSALT study
 Ecological analysis
◦ Increase in salt intake by 100 mmol/day was
associated with increase in SBP by 7.1 mm Hg
 Individual level analysis
◦ increase by 1.6 mm Hg of SBP
From Elliott et al, BMJ 1996
Ecological studies: Advantages
 Use existing (often routinely collected) data
 Quick and cheap
 Useful to general hypotheses
 Differences in both exposure and outcome rates may be
large, which increases the likelihood to find an association
 Some exposures are difficult to measure in individuals and
area-based measures are used instead (e.g. air pollution),
and some exposures are inherently ecological (e.g. income
inequality)
Ecological studies: Disadvantages
 Confounding: the groups, which are compared (e.g.
countries) usually differ in many other factors than the
exposure of interest. It is often impossible to reliably
control for confounders.
 There can be systematic differences in measurements of
exposures and diseases (e.g. coding of causes of death)
between populations.
 Ecological fallacy: ecological studies compare groups but
results are extrapolated to individuals.
Cohort studies
time
direction of enquiry
Advantages of cohort study
- Temporal sequence is clear (exposure before disease)
- Less prone to ‘reverse causality’
- Allows calculation of disease incidence
- Can examine many exposures simultaneously
- Multiple outcomes can be examined
Disadvantages of cohort study
- Exposure may change over time
- Some diseases take years/decades to develop so may not
be suitable
- Findings might not be relevant at end of study
- High costs because large sample and long duration
- Participant burden
- Loss to follow-up usually depends on outcome of interest
(selection bias)
- Assessment of causality problematic in observational
setting (although less problematic in cohort than other
types of observational studies)
Some well-known cohort studies
 British Birth Cohorts
◦ Millennium Cohort Study
◦ 1970 British Cohort Study (BCS70)
◦ 1958 National Child Development Study
◦ 1946 National Survey of Health and
Development
 Studies of specific diseases (e.g. cardiovascular disease):
◦ Whitehall II study
◦ Framingham Study
◦ HAPIEE (Health,Alcohol and Psychosocial
Indicators in Eastern Europe)
Summary of cohort studies
 Exposure measured usually in healthy
individuals
 Follow up
 Incidence
 Time consuming & expensive
 Temporality clear
 Possibly the “best” observational design
Case-control studies
CohortStart
Unexposed
Exposed
All healthy Follow-up (wait)
Disease
assessment
Controls
Cases
Start
Look back
Case-Control
Case-control studies are
 Ideal for rare diseases
 Usually “retrospective” in design
 Relatively quick
 Relatively cheap
Time
“Now”
Cases
Controls
Basic steps in a case-control study
 Measurement of exposure
 Comparing frequency of exposure in cases and controls
Strengths of case-control studies
 Quick (cases already exist, no need to wait)
 Cheap (not necessary to examine large
number of people)
 Can examine many exposures
 Suitable to study rare diseases
 Suitable to study stable exposures (eg
genetic markers)
Weaknesses of case-control studies
 Not suitable for rare exposure
 Prone to misclassification of exposure
 Prone to reverse causation (people with
disease may have changed their behaviour)
Intervention studies
Basic features of intervention studies
 An intervention study involves an intentional
change in some aspect of environment or status
of the subjects of the investigation.
 Intervention studies differ from observational
studies in that the researcher seeks to compare
two or more groups that differ as a result of
deliberate action rather than natural or found
variation.
Everything except the
intervention is (hoped to be)
the same in the two groupsDefined study
sample
Intervention
group
Control
group
Measure
outcome
Measure
outcome
Randomisation to two groups
Key issues in RCTs
 Careful entry criteria
 Assessment (Pre- & Post-intervention)
 Randomisation
 Allocation Concealment
 Blinding (Masking)
The aim of randomisation is to…
create groups that are comparable with
respect to known or unknown
confounding factors
There are two steps in the process
1. Generating an unpredictable allocation
sequence e.g. tossing a coin, using a
computer random number generator
2. Concealing the allocation sequence from
the investigators
Not always possible
Allocation concealment
 … is making sure that neither investigator
nor patient can predict group assignment
Adequate methods
Off-site randomisation e.g. needing a phone
call
Sequentially numbered, sealed, opaque
envelopes
Blinding
 If participants or researchers know whether
participant is receiving intervention then
there is risk of:
◦ Measurement error
◦ Different investigations & care study group etc.
◦ Acceptability bias (Researchers influence participants
behaviour)
 Different “levels” of blinding: can blind
participants, researchers and/or statisticians
or none
Advantages of RCTs
 Experimental: groups treated
similarly except intervention
 Randomisation: characteristics
similarly distributed
 Blinding
patients
investigators
statisticians
Tells us that
difference at the
end is only due to
intervention
Gold-standard epidemiological study design to
assess effectiveness of interventions
Summary
 Intervention studies are experiments
 RCTs are the gold-standard design for
assessing the effectiveness of interventions
 Simple concept but many key features - need
to carry out properly
 Randomisation is the most important, but
others
 Not always applicable – PH interventions are
usually more complex than a clear-cut simple
experiment
hierarchy of major study designs
systematic review of RCTs
RCT
cohort
case control
interventional
observational
validity
ecological
cross-sectional
Applications of different observational and analytical study designs
Ecological Cross
sectional
Case
control
Cohort
Investigation of rare disease ++++ - +++++ Investigation
of rare exposures ++ - - +++++
Examining multiple outcomes + ++ - +++++
Studying multiple exposures ++ ++ ++++ +++
Measurement of time
relationships between expo and
outcome
+ - + +++++
Direct measurement of incidence - - + +++++
Investigation of long latent period - - +++ +++