Transcript Epi-2 Lecture 1

Basic Study Designs
in Observational
Epidemiology
Epidemiologic reasoning
• To determine whether a statistical association
exists between a presumed risk factor and
disease
• Risk Factor: Antecedents of adverse
health outcomes that remain associated
with the outcomes after adjustments for
measured potential confounders (Greenland
et al, Epidemiology 2004;15:529-535)
Epidemiologic reasoning
• To determine whether a statistical
association exists between a presumed
risk factor and disease
• To derive inferences regarding a
possible causal relationship from the
patterns of the statistical associations
To determine whether a statistical
association exists between a
presumed risk factor and a disease
• Observational studies using populations or groups of
individuals as units of observation
– Descriptive studies (prevalence, incidence, trends)
– Analysis of birth cohorts (cohort, age, period effects)
– Ecological studies
• Observational studies using individuals as units of
observation
–
–
–
–
Cohort studies
Case-control studies
Cross-sectional studies
Other (nested case-control, case-crossover study)
Studies using groups as units of observation
• ANALYSIS OF BIRTH COHORTS
– Cohort-, age-, period-effects
– Classic example:
WH Frost: The age selection of mortality from tuberculosis
in successive decades. Am J Hyg 1939;30:91-6.
Prevalence (per 1000)
50
1995 cross-sectional survey
40
30
1995
20
10
1965
1985
1975
Cross-sectional
surveys
0
0
10
20
30
40
50
Age (years)
60
70
80
Prevalence (per 1000)
50
Birth
cohorts
Born in 1960
40
30
1950
1940 1930
20
1995
1920
10
1965
1985
1975
Cross-sectional
surveys
0
0
10
20
30
40
50
Age (years)
60
70
80
• Age effect: Change in the rate according to
age, irrespective of birth cohort and calendar
time
• Cohort effect: Change in the rate
according to year of birth, irrespective of age
and calendar time
• Period effect: Change in the rate affecting
an entire population at some point in time,
irrespective of birth cohort and age.
Cohort effect: rates are changing
from cohort to cohort regardless of age
Prevalence (per 1000)
Age effect: rates vary by age in each cohort
50
Birth
cohorts
Born in 1960
40
30
1950
1940 1930
20
1995
1920
10
1965
1985
1975
Cross-sectional
surveys
0
0
10
20
30
40
50
Age (years)
60
70
80
Prevalence (per 1000)
Period effect: Event in 1945 changes all cohorts in
the corresponding ages
Birth
cohorts
50
Born in:
1920
40
1910
30
1900
20
1890
1880
10
0
0
10
20
30
40
50
Age (years)
60
70
80
Studies using groups as units of observation
• ECOLOGIC STUDIES
– To assess the correlation between a presumed
risk factor and an outcome, mean values of the
outcome (e.g., rate, mean) are plotted against
mean values of the factor (e.g., average per capita
fat intake), using groups as units of observation
– Groups can be defined by place (geographical
comparisons) or time (temporal trends).
A plot of the population of Oldenburg at the end of each year against
the number of storks observed in that year, 1930-1936.
Ornitholigische Monatsberichte 1936;44(2)
Relation between sodium (Na) excretion and age increase in systolic blood
pressure (SBP) in centers in the INTERSALT cohort*
*Elliot, in Marmot and Elliot (eds.): Coronary Heart Disease Epidemiology, Oxford, 1992, pp.166-78.
Ecological fallacy
“The bias that may occur because an
association observed between
variables on aggregate levels does no
necessarily represent the association
that exists at the individual level.”
Last: Dictionary of Epidemiology, 1995
Example of ecological bias*
Population A
$10.5K
$34.5K
$28.5K
$12.2K
$45.6K
$17.5K
$19.8K
Traffic injuries: 4/7=47%
Mean income: $23,940
Population B
$12.5K
$32.5K
$24.3K
$10.0K
$14.3K
$38.0K
$26.4K
Traffic injuries: 3/7=43%
Mean income: $22,430
Population C
$28.7K
$30.2K
$13.5K
$23.5K
Mean income: $21,410
*Based on: Diez-Roux, Am J Public Health 1998;88:216.
$10.8K
$22.7K
$20.5K
Traffic injuries: 2/7=29%
Traffic injuries (%)
60
Ecologic
analysis
50
40
30
Higher income is
associated with
higher injury rate
20
10
0
21
22
23
24
Mean income (US$, in 1000)
25
Example of ecological bias*
Population A
$10.5K
$34.5K
$28.5K
$12.2K
$45.6K
$17.5K
$19.8K
Traffic injuries: 4/7=47%
Mean income: $23,940
Population B
$12.5K
$32.5K
$24.3K
$10.0K
$14.3K
$38.0K
$26.4K
Traffic injuries: 3/7=43%
Mean income: $22,430
Population C
$28.7K
$30.2K
$13.5K
$23.5K
Mean income: $21,410
*Based on: Diez-Roux, Am J Public Health 1998;88:216.
$10.8K
$22.7K
$20.5K
Traffic injuries: 2/7=29%
Traffic injuries (%)
60
Ecologic
analysis
50
40
30
Higher income is
associated with
higher injury rate
20
10
0
21
22
23
24
25
Mean income (US$, in 1000)
Individual-based
analysis
Non cases
Injury cases have
lower mean income
than non cases
Injury
cases
0
10
20
30
Mean income (1000 US$)
40
• Which of the two levels of inference is wrong?
– Concluding that high income is a risk factor for injuries
(based on the ecologic data) is subject to ecologic fallacy.
– BUT … concluding that, because injury cases tend to have
lower income, communities with higher average income
should have lower injury rates is also wrong!
• The real problem is cross-level reference*
– Using ecologic data to make inference at the individual level
(ecologic fallacy).
– Or using the individual data to make inferences at the group
(population level).
• When used to make inferences at the proper
level, both approaches might be right.
*Morgenstern: Ann Rev Public Health 1995;16:61-81.
Types of ecologic variables
• Analogs of individual-level characteristics
– Aggregate measures (proportion, mean)
• Prevalence of disease
• Mean saturated fat intake
– Environmental measures
• Air pollution
• Global measures
• Health care system
• Gun control law
• Herd immunity
Ecologic studies are the design
of choice in certain situations:
• When the level of inference of interest is at the
population level
– Food availability (e.g., Goldberger et al: Public Health Rep
1916;35:2673-714).
– SES inequality and health
– Effects of tax hikes in cigarette sales
• When the variability of exposure within the population
is limited
– Salt intake and hypertension (Elliot, 1992)
– Fat intake and breast cancer (Wynder et al, 1997)
Hypothetical data on
individuals from a
World-wide population
Strong positive (linear)
association
Usual daily salt intake
Hypothetical data on
individuals from a
World-wide population
Individuals from
country A
Usual daily salt intake
No association
Usual daily salt intake
Hypothetical data on
individuals from a
World-wide population
Country A
Country B
Country C
Country D
Country E
Country F
Country G
Usual daily salt intake
Hypothetical ecologic
data from 7 countries
Country A
Country B
Country C
Country D
Country E
Country F
Country
G
Strong
positive (linear)
association
Mean usual daily salt intake
Relation between sodium (Na) excretion and age increase in systolic blood
pressure (SBP) in centers in the INTERSALT cohort*
*Elliot, in Marmot and Elliot (eds.): Coronary Heart Disease Epidemiology, Oxford, 1992, pp.166-78.
Studies based on individuals
1.- Cohort studies
Cohort
Outcome
Death
Disease
Recurrence
Recovery
Suspected
Exposure
Time
Studies based on individuals
1.- Cohort studies
Diseased
Non diseased
Ince
Exposed
RR
Non
Exposed
Incē
Time
Cohort study
Losses to follow-up
Events
Initial
pop
time
Final
pop
Studies based on individuals
2.- Case-control studies
Non
Diseased diseased
Exposed
Non
Exposed
Odds expD
Odds expD-
OR
Case-control study
Losses
Cases
Controls
Hypothetical
pop
time
Case-control study
Losses
Cases
Controls
Hypothetical
Recruiting
only cases with longest survival (Prevalent cases)
pop
Risk of durationtime
(incidence-prevalence) bias
Cross-sectional study
Snapshot of prevalent
cases/non-cases
Nested case-control study
(“Incidence density sampling”)
Initial
pop
time
time
Final
pop
“Risk set”
Example of nested case-control study
US Air Force Cohort Study (Grayson, Am J Epidemiol 1996;143:480-6)
Cohort: 880,000 male members of US Air Force employed for at least
one year between 1970-89 (variable length of follow-up).
• Cases: 230 newly developed cases of malignant brain tumor 1970-89
• Controls: 920 non-case employees, 4 for each case’s risk-set, matched
by age, race, and length of follow-up.
Cases
Initial
pop
Controls
Age-race-senior military rank-adjusted odds ratios in brain tumor cases
and controls without brain tumors, according to exposure to very low
frequency electromagnetic fields or to radiofrequency/microwave
(Grayson JK, Am J Epidemiol 1996;143:480-486)
Exposure to very low
frequency electromagnetic
fields (EMFs)
No. of
cases
No. of
controls
Odds
ratio
95% CI
Ever exposed*
129
441
1.3
0.95, 1.7
Never exposed
101
479
1.0
Ever exposed
94
281
1.4
Never exposed
136
639
1.0
Exposure to
radiofrequency/microwave
EMFs**
1.0, 1.9
*Example: power general specialists, telecommunications equipment repair men
**Above permissible exposure limits (10 mW/cm2)
Case-cohort study
Initial
pop
time
Final
pop
Example of case-cohort study
Association between CMV antibodies and incident coronary heart
disease (CHD) in the Atherosclerosis Risk in Communities (ARIC)
Study
(Sorlie et al: Arch Intern Med 2000;160:2027-32)
Cohort: 14,170 adult individuals (45-64 yrs at baseline) from 4 US
communities (Jackson, Miss; Minneapolis, MN, Forsyth Co NC;
Washington Co, MD), free of CHD at baseline.
Followed-up for up to 5 years.
• Cases: 221 incident CHD cases
• Controls: Random sample from baseline cohort, n=515 (included 10
subsequent cases).
“The population with the highest antibody levels of CMV (approximately
the upper 20%) showed an increased relative risk (RR) of CHD of
1.76 (95% confidence interval, 1.00-3.11), adjusting for age, sex, and
race.”
Case-cohort study
N14,000
Option 1= thaw serum samples
of 14,000 persons, classify
by CMV titer (+) or (-), and followup to calculate incidence in each
group (exposed vs. unexposed)
Option 2: Case-cohort study
Initial
pop
Time (5 years)
Final
pop
• When are nested designs (case-cohort or
nested case-control) the best choice?
In well defined cohorts when additional (expensive or
burdensome) information needs to be collected
– Laboratory determination in samples from specimen
repository (e.g., serum bank).
– Additional record abstraction (e.g., medical, occupational
records).
• Analytical techniques (analogous to
methods used in cohort studies, matched
case-control studies) are available.
A few notes on “Matching”
• Most frequently used in case-control studies
• Frequency vs. individual matching
• Advantages:
– Intuitive, easy to explain
– Guarantees certain degree of comparability in small studies
– Efficient (if matching on a strong confounder)
• Disadvantages:
– Costly, usually logistically complicated
– Inefficient (if matching on a weak confounder)
– Questionable representativiness of control group (limits its
use for other case-control comparisons)
– Cannot study the matching variable (and additive interaction)
– Possibility of residual confounding
A special type of case-control study: the case-crossover study
• Useful when exposures that vary over time can precipitate
acute events, such as sudden cardiac deaths, asthma
episodes, etc.
• Cases serve as their own controls: The subject’s time of event
of interest (e.g., death) is the case period, and the subject’s
other times comprise the control period
•Advantages:
–Each participant is considered a matched stratum in a casecontrol study (self-matching) where “cases” and “controls” are
case and control times (no control selection bias)
–Controls for confounding by unchanged variables (sex, genetic
factors, mental health, etc.)
•Disadvantages:
–Assumes no “carry over” (cumulative) effect of exposure of
interest
–No confounding or interaction by time-related variables (e.g.,
ambient temperature, day of the week)
•Challenges:
–Lag time must be taken into account (relevant exposure period)
A special type of case-control study: the case-crossover
study– Example: Valent et al, Pediatrics 2001;107:e23
• Objective: to evaluate the association between sleep (and
wakefulness) duration and childhood unintentional injury
• 292 unintentionally injured children
• Case period: 24 hours preceding injury
• Control period: 25-48 hours preceding injury
• Analysis: matched-pair and conditional logistic regression
• Adjustment for day of the week (week-end vs. weekday) and
activity risk level (higher vs. lower level of energy)
Odds Ratios and 95% CIs for Sleeping Less than 10 Hours a Day
Study
subjects
n
Ca+
Co+
Ca+
Co-
CaCo+
CaCo-
RR
95% CI
All cases
292
62
26
14
190
1.86
.97, 3.55
Boys
181
40
21
9
111
2.33
1.07, 5.09
Girls
111
22
5
5
79
1.00
0.29, 3.45
(Valent et al, Pediatrics 2001;107:e23)
•Case period: 24 hours preceding injury
•Control period: 25-48 hours preceding injury
Threats of Validity in Case-Crossover Studies
(Maclure M, Am J Epidemiol 1991;133:144-53)
• Within-individual confounding
– No confounding by the individual’s characteristics that remain constant, but
there can be confounding by variables that vary over time.
•Example: A person who drinks coffee only after an anger outburst
• Selection bias
– No bias in selection of control periods
– Biased case-period selection is possible
• Case-crossover study of incident nonfatal myocardial infarction and anger
episode (Moller et al, Psychosom Med 1999;61:842-9)
– “Survival bias
implies that if cases being exposed to anger have a
better prognosis for surviving MI than those not exposed to anger, a
study of only nonfatal cases would overestimate the relative risk of
MI. Likewise, if cases exposed to anger right before their MI are less
inclined to participate, this would result in an underestimation.”
Threats of Validity in Case-Crossover Studies
(cont.) (Maclure M, Am J Epidemiol 1991;133:144-53)
• Information bias
– When interviews are done at the time of the event, quality of the information
obtained from the case (or a proxy) about the case (hazard) period may differ
from that about the control period (e.g., when the case period is the 24-hr
period preceding the event, and the control period is the 25 to 48-hour
preceding the event)
• Bias can go in either direction:
– Faulty memory regarding the control period
– Exaggeration or denial of exposure in the case period
• External validity
– “In principle, generalizable to all acute-onset outcomes hypothesized to be
caused by brief exposures with transient effects.” (Maclure M)