Epi-2 Lecture 1

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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

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To determine whether a statistical association
exists between a presumed risk factor and a
disease

• 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
• Studies using individuals as units of observation
• Randomized clinical trials
• Cohort studies
• Case-control studies
• Cross-sectional studies
• Other (nested case-control, case-crossover study)

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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 could be defined by place (geographical
  comparisons) or time (temporal trends).

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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 193644(2)
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Relation between Anopheles inoculation and
incidence of Plasmodium Falciparum parasitemia in
cohorts of children in Western Kenya
McElroy et al Am J Epidemiol 1997145945-56.
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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
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Example of ecological bias
Traffic injuries 4/747
Based on Diez-Roux, Am J Public Health
199888216.
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Higher income is associated with higher injury
rate
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Example of ecological bias
Traffic injuries 4/747
Based on Diez-Roux, Am J Public Health
199888216.
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Higher income is associated with higher injury
rate
Injury cases have lower mean income than non cases
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• 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
• Individuals with a lower income are more likely
  to be injured.
• In communities with higher average incomes, there
  is a greater number of cars, thus exposing lower
  income individuals to injuries.

Morgenstern Ann Rev Public Health 19951661-81.
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Types of ecologic variables

• Analogs of individual-level characteristics
• Aggregate measures (proportion, mean)
• Prevalence of disease
• Mean saturated fat intake
• Percentage with less than high school education
• Environmental measures
• Air pollution
• Global measures
• Health care system
• Gun control law
• Herd immunity

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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 1916352673-714).
• 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)

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Hypothetical data on individuals from a
World-wide population
Systolic blood pressure (mm Hg)
Usual daily salt intake
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Hypothetical data on individuals from a
World-wide population
Systolic blood pressure (mm Hg)
Usual daily salt intake
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Hypothetical data on individuals from a
World-wide population
Systolic blood pressure (mm Hg)
Usual daily salt intake
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Hypothetical ecologic data from 7 countries
Mean systolic blood pressure (mm Hg)
Mean usual daily salt intake
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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.
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Studies based on individuals Prospective Studies
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Studies based on individuals Prospective Studies
Experimental (Randomized clinical trial)
Study Population
Random allocation
Intervention
Control
Follow-up
Follow-up
Outcome
Outcome
Cohort Study
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Studies based on individuals Prospective Studies
Experimental (Randomized clinical trial)
Study Population
Random allocation
Intervention
Control
Follow-up
Follow-up
Outcome
Outcome
Cohort Study
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Studies based on individuals1.- Cohort studies
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Studies based on individuals1.- Cohort studies
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Cohort study
Losses to follow-up
Events
Final pop
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Cohort study
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Cohort StudiesStrengths

• Allows calculation of incidence
• Time sequence is clear (exposure ?outcome)
• Reduces potential for bias
• Allows calculation of all measures of association
• Multiple outcomes can be assessed
• Multiple exposures can be assessed
• New hypothesis can be tested as time goes by
• Efficient ways to evaluate associations
• Stored specimens can be analyzed later for new
  analytes / risk factors

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Cohort StudiesAdditional Advantages

• Can incorporate changes in exposures and
  confounders over time
• As participants age
• As exposure accumulates
• Exposures and outcomes do not (necessarily) have
  to be identified a priori
• New endpoints can be assessed e.g., cancer
• Examination of baseline associations
• Cross-sectional bias less likely with subclinical
  outcomes
• The cohort as an epidemiologic laboratory
• Ancillary studies can be done

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Cohort StudiesAdditional Advantages

• Can incorporate changes in exposures and
  confounders over time
• As participants age
• As exposure accumulates
• Exposures and outcomes do not (necessarily) have
  to be identified a priori
• New endpoints can be assessed e.g., cancer
• Examination of baseline associations
• Cross-sectional bias less likely with subclinical
  outcomes
• The cohort as an epidemiologic laboratory
• Ancillary studies can be done

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Cohort StudiesAdditional Advantages

• Can incorporate changes in exposures and
  confounders over time
• As participants age
• As exposure accumulates
• Exposures and outcomes do not (necessarily) have
  to be identified a priori
• New endpoints can be assessed e.g., cancer
• Examination of baseline associations
• Cross-sectional bias less likely with subclinical
  outcomes
• The cohort as an epidemiologic laboratory
• Ancillary studies can be done

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Cohort StudiesAdditional Advantages

• Can incorporate changes in exposures and
  confounders over time
• As participants age
• As exposure accumulates
• Exposures and outcomes do not (necessarily) have
  to be identified a priori
• New endpoints can be assessed e.g., cancer
• Examination of baseline associations
• Cross-sectional bias less likely with subclinical
  outcomes
• The cohort as an epidemiologic laboratory
• Ancillary studies can be done

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Cohort StudiesAdditional Advantages

• Can incorporate changes in exposures and
  confounders over time
• As participants age
• As exposure accumulates
• Exposures and outcomes do not (necessarily) have
  to be identified a priori
• New endpoints can be assessed e.g., cancer
• Examination of baseline associations
• Cross-sectional bias less likely with subclinical
  outcomes
• The cohort as an epidemiologic laboratory
• Ancillary studies can be done
• Rich database for analyses

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Studies based on individuals2.- Case-control
studies
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Case-control study
Losses
Hypothetical pop
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Case-control study
Losses
Recruiting only cases with longest survival
(Prevalent cases) Risk of duration
(incidence-prevalence) bias
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INCIDENCE-PREVALENCE BIAS
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• CASE-CONTROL STUDY INCLUDING ALL INCIDENT CASES
  AND NON-CASES

Exposed? Cases Controls
Yes 4 96
No 4 96
8 192
Assumption All non-cases survive through the end
of the follow-up
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• CASE-CONTROL STUDY INCLUDING ALL INCIDENT CASES
  AND NON-CASES

Exposed? Cases Controls
Yes 4 96
No 4 96
8 192
CASE-CONTROL STUDY INCLUDING ONLY POINT PREVALENT
CASES, BUT ALL NON-CASES

Exposed? Cases Controls
Yes 1 96
No 4 96
5 192
SELECTION/SURVIVAL BIAS (ALSO KNOWN AS
PREVALENCE-INCIDENCE BIAS)
Assumption All non-cases survive through the end
of the follow-up
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Results from cross-sectional surveys can be
analyzed in a prospective or case-control mode
Disease Disease
Exposure Yes No Prevalencedis
Yes a b a/ab
No c d c/cd


Disease Disease
Exposure Yes No
Yes a b
No c d
Odds exp a/c b/d
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• CASE-CONTROL STUDY INCLUDING ALL INCIDENT CASES
  AND NON-CASES

Exposed? Cases Controls
Yes 4 96
No 4 96
8 192
PREVALENCE OF DISEASE BY EXPOSURE
Disease?
Exposed? Yes No Total
Yes 1 96 97
No 4 96 100

PR 1/97 4/100 0.26
SELECTION/SURVIVAL BIAS (ALSO KNOWN AS
PREVALENCE-INCIDENCE BIAS)
Assumption All non-cases survive through the end
of the follow-up
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Cross-Sectional Vs. Retrospective Case-Control
Studies
Key concept How caseness and exposure are
ascertained
ANOTHER TYPE OF CROSS-SECTIONAL BIAS REVERSE
CAUSALITY
Cross-Sectional Exposure Assessment Association
of Low Serum Carotenoids with Age-Related Macular
Degeneration
Total Carotenoids Cases Controls
1.024 µmol/L (exposed) 107 115
gt 1.024 µmol/L (unexposed) 284 462
Total 391 577
Exposure Odds 0.381.0 0.251.0
Odds Ratio 1.5
IT IS NOT POSSIBLE TO DETERMINE WHAT CAME FIRST
(EXPOSURE OR OUTCOME). THUS, INDIVIDUALS WITH
AGE-RELATED MACULAR DEGENERATION MAY CHANGE THEIR
DIETS, WHICH IN TURN MAY RESULT IN LOW
CONCENTRATIONS OF TOTAL CAROTENOIDS ? REVERSE
CAUSALITY
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Cross-sectional StudiesNational Center for
Health Statistics (NCHS)

• National Health and Nutrition Examination Survey
  (NHANES)
• 20,000 individuals
• Oversampled children, agegt65, minorities
• Questionnaires, physical exam, laboratory data
• National Health Interview Survey (NHIS)
• National Immunization Survey (NIS)
• National Survey of Family Growth (NSFG)

www.cdc.gov/nchs
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Cross-sectional survey
Snapshot of prevalence at time of a
cross-sectional survey
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Cross-sectional StudiesWhat can we learn?
Descriptions / Distributions Standardized
centile curves of body mass index for Japanese
children and adolescents based on the 1978-1981
national survey data. Ann Hum Biol. 2006
Jul-Aug33(4)444-53.
Prevalence The prevalence of oral mucosal
lesions in U.S. adults data from the Third
National Health and Nutrition Examination Survey,
1988-1994. J Am Dent Assoc. 2004
Sep135(9)1279-86
Trends in prevalence Thirty-year trends in
cardiovascular risk factor levels among US adults
with diabetes National Health and Nutrition
Examination Surveys, 1971- 2000 Am J Epidemiol.
2004 Sep 15160(6)531-9
Association of exposure with prevalence of
disease Prevalence of urinary schistosomiasis
and HIV in females living in a rural community of
Zimbabwe does age matter? Trans R Soc Trop Med
Hyg. 2006 Oct 23
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Cross-sectional Studies

• Baseline examination of randomized trials
• Cross-sectional study of health-related quality
  of life in African Americans with chronic renal
  insufficiency the African American Study of
  Kidney Disease and Hypertension Trial.
• Am J Kidney Dis. 2002 Mar39(3)513-24.
• Baseline examination of cohort studies
• Association of kidney function and hemoglobin
  with left ventricular morphology among African
  Americans the Atherosclerosis Risk in
  Communities (ARIC) study.
• Am J Kidney Dis. 2004 May43(5)836-45.

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Cross-sectional StudiesStrengths and Limitations

• Strengths
• Primary method of estimating prevalence
• Logistically efficient
• Relatively fast (no follow-up required)
• Can enroll large numbers of participants
• Large surveys can be used for many exposures and
  diseases
• Often generalizable can oversample smaller
  subpopulations
• Limitations
• Large numbers needed for rare exposures /
  outcomes
• No information on timing of outcome relative to
  exposure (temporality)
• Includes only those individuals alive at the time
  of the study
• Prevalence-incidence bias

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Case-control studies within a defined cohort

• Case-Cohort Studies
• Nested Case-Control Studies

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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 20001602027-32) 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, n515 (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.
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Case-cohort study
N14,000
Option 1 thaw serum samples of 14,000 persons,
classify by CMV titer () or (-), and follow- up
to calculate incidence in each group (exposed vs.
unexposed)
Option 2 Case-cohort study
Initial pop
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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 20001602027-32) 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, n515 (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.
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case
(Incidence density sampling)
loss
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Example of nested case-control study Inflammatory
Markers and CHD Risk (Pai JK, et al, New Eng J
Med 20043512599-610) Cohorts Nurses Health
Study (30-55 yrs old, n 121 700 nurses) and
Health Professionals Follow-up Study (40-75 yrs
old, n 51 529) (follow-up 6 years and 8 years,
respectively) Cases 239 women and 265 men who
developed an MI Controls Selected by risk set
sampling using 21 ratio, matched for age,
smoking, and date of blood sampling from
participants free of cardiovascular disease at
the time CHD was diagnosed in cases.
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Example of nested case-control study Inflammatory
Markers and CHD Risk (Pai JK, et al, New Eng J
Med 20043512599-610) Cohorts Nurses Health
Study (30-55 yrs old, n 121 700 nurses) and
Health Professionals Follow-up Study (40-75 yrs
old, n 51 529) (follow-up 6 years and 8 years,
respectively) Cases 239 women and 265 men who
developed an MI Controls Selected by risk set
sampling using 21 ratio, matched for age,
smoking, and date of blood sampling from
participants free of cardiovascular disease at
the time CHD was diagnosed in cases.
Cases
Controls
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Rate Ratios of Coronary Heart Disease During
Follow-up According to Quintiles of C-Reactive
Protein at Baseline, Nurses Health Study (Women)
and Health Professionals Study (Men)
Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein
1 2 3 4 5
Women
Median mg/liter 0.50 1.18 2.20 4.02 9.14
Rate Ratios 1.0 1.23 0.89 1.22 1.61
Men
Median mg/liter 0.27 0.60 1.08 2.05 5.24
Rate Ratios 1.0 1.75 1.74 2.14 2.55
Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors
(Pai JK, et al, New Eng J Med 20043512599-610)
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Rate Ratios of Coronary Heart Disease During
Follow-up According to Quintiles of C-Reactive
Protein at Baseline, Nurses Health Study (Women)
and Health Professionals Study (Men)
Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein Quintile of Plasma Level of C-Reactive Protein
1 2 3 4 5
Women
Median mg/liter 0.50 1.18 2.20 4.02 9.14
Rate Ratios 1.0 1.23 0.89 1.22 1.61
Men
Median mg/liter 0.27 0.60 1.08 2.05 5.24
Rate Ratios 1.0 1.75 1.74 2.14 2.55
Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors Adjusted for socio-demographic and cardiovascular risk factors
(Pai JK, et al, New Eng J Med 20043512599-610)
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Nested Case-Control Design
Case-cohort Design

• It is best for time-dependent exposures

ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE
COHORT
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Nested Case-Control Design
Case-cohort Design

• It is best for time-dependent exposures

ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE
COHORT
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Nested Case-Control Design
Case-cohort Design

• It is best for time-dependent exposures

ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE
COHORT
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Nested Case-Control Design
Case-cohort Design

• It is best for time-dependent exposures

ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE
COHORT
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Nested Case-Control Design
Case-cohort Design

• It is best for time-dependent exposures
• It automatically matches for length of follow
  (and for previous losses).
• (Disadvantage for each case, a different matched
  control sample must be chosen.)

ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE
COHORT
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Nested Case-Control Design
Case-cohort Design

• It is best for time-dependent exposures
• It automatically matches for length of follow
  (and for previous losses).
• (Disadvantage for each case, a different matched
  control sample must be chosen.)

ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE
COHORT
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Nested Case-Control Design
Case-cohort Design

• It is best for time-dependent exposures
• It automatically matches for length of follow
  (and for previous losses).
• (Disadvantage for each case, a different matched
  control sample must be chosen.)

ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE
COHORT
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• 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.

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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 subjects
  time of event of interest (e.g., death) is the
  case period, and the subjects other times
  comprise the control period

• Advantages
• Each participant is considered a matched stratum
  in a case-control study (self-matching) where
  cases and controls are case and control times
  (no control selection bias)
• Self-matches for confounding variables that do
  not change over time (sex, genetic factors, etc.)

• Disadvantages
• Assumes no carry over (cumulative) effect of
  exposure of interest
• Assumes 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)

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A special type of case-control study the
case-crossover study Example Valent et al,
Pediatrics 2001107e23

• Objective to evaluate the association between
  sleep (and wakefulness) duration and childhood
  unintentional injury
• Sample 292 unintentionally injured children
• Case period 24 hours preceding injury
• Control period 25-48 hours preceding injury
• Definition of exposure Child slept lt10 hs
• 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)

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Odds Ratios and 95 CIs for Sleeping Less than 10
Hours
Study subjects n Ca Co Ca Co- Ca- Co Ca- Co- OR 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 2001107e23)

• For ascertainment of exposure
• Case period 24 hours preceding injury
• Control period 25-48 hours preceding injury

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Threats of Validity in Case-Crossover
Studies(Maclure M, Am J Epidemiol
1991133144-53)

• Within-individual confounding
• No confounding by the individuals
  characteristics that remain constant, but there
  can be confounding by variables that vary over
  time.
• Example A person who drinks coffee only in
  colder days. If colder days precipitate the event
  (e.g., angina pectoris), the association with
  coffee drinking can be explained away by the fact
  that the day was colder.
• Selection bias
• Case-crossover study of incident nonfatal
  myocardial infarction and anger episode (Moller
  et al, Psychosom Med 199961842-9)
• 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.

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Threats of Validity in Case-Crossover
Studies(cont.) (Maclure M, Am J Epidemiol
1991133144-53)

• Information bias
• Recall bias When interviews are done at the
  time of the event, quality of the information
  obtained from the patient (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)