EPI546_Lecture_8.ppt

Mathew J. Reeves, PhD
© Dept. of Epidemiology, MSU
1
Lecture 8 – Study Design I
XS and Cohort Studies
Mathew J. Reeves BVSc, PhD
Associate Professor, Epidemiology
EPI-546 Block I

2
Objectives - Concepts
• Uses of risk factor information
• Association vs. causation
• Architecture of study designs (Grimes I)
• Cross sectional (XS) studies
• Cohort studies (Grimes II)
• Measures of association – RR, PAR, PARF
• Selection and confounding bias
• Advantages and disadvantages of cohort studies

3
Objectives - Skills
• Recognize different study designs
• Define a cohort study
• Explain the organization of a cohort study
• Distinguish prospective from retrospective
• Define, calculate, interpret RR, PAR and PARF
• Understand and detect selection and confounding
bias

4
“Risk Factor” – heard almost daily
Cholesterol and heart disease
HPV infection and cervical CA
Cell phones and brain cancer
TV watching and childhood obesity
However, “association does not mean causation”!

5
Why care about risk factors? Fletcher lists the ways
risk factors can be used:
• Identifying individuals/groups “at-risk”
• But ability to predict future disease in individual patients is very
limited even for well established risk factors
• e.g., cholesterol and CHD
• Causation (causative agent vs. marker)
• Establish pretest probability (Bayes’ theorem)
• Risk stratification to identify target population
• Example: Age > 40 for mammography screening
• Prevention
• Remove causative agent & prevent disease

6
Predicting disease in individual patients
Fig. Percentage distribution of serum cholesterol levels (mg/dl) in men aged
50-62 who did or did not subsequently develop coronary heart disease
(Framingham Study)

7
Causation vs. Association
• An association between a risk factor and disease can
be due to:
• the risk factor being a cause of the disease (= a causative
agent) OR
• the risk factor is NOT a cause but is merely associated with
the disease (= a marker)
• Must guard against thinking that A causes B when
really B causes A (reverse causation).
• e.g. sedentariness and obesity.

8
A B
A B
A B
C
A B

9
Prevention
• Removing a true cause → ↓ disease incidence.
• Decrease aspirin use → ↓ Reye’s Syndrome
• Discourage prone position
“Back to Sleep” → ↓ SIDS

10
Back-To-Sleep Campaign
Began in 1992

11
Architecture of study designs
• Experimental vs. observational
• Experimental studies
• Randomization?
– RCT vs. quasi-randomized or natural experiments
• Observational studies
• Analytical vs. descriptive
• Analytical
– XS, Cohort, CCS
• Descriptive
– Case report, case series

12
Grimes DA and Schulz KF 2002. An overview of clinical research. Lancet 359:57-61.

13
Grimes DA and Schulz KF 2002. An overview of clinical research. Lancet 359:57-61.

14
Cross-sectional studies
• Also called a prevalence study
• Prevalence measured by conducting a survey of the population
of interest e.g.,
• Interview of clinic patients
• Random-digit-dialing telephone survey
• Mainstay of descriptive epidemiology
• patterns of occurrence by time, place and person
• estimate disease frequency (prevalence) and time trends
• Useful for:
• program planning
• resource allocation
• generate hypotheses

15
Cross-sectional Studies
• Select sample of individual subjects and report
disease prevalence (%)
• Can also simultaneously classify subjects
according to exposure and disease status to draw
inferences
• Describe association between exposure and disease
prevalence using the Odds Ratio (OR)

16
• Examples:
• Prevalence of Asthma in School-aged Children in Michigan
• Trends and changing epidemiology of hepatitis in Italy
• Characteristics of teenage smokers in Michigan
• Prevalence of stroke in Olmstead County, MN

17
Concept of the Prevalence “Pool”
New cases
Death
Recovery

18
• Advantages:
• quick, inexpensive, useful
• Disadvantages:
• uncertain temporal relationships
• survivor effect
• low prevalence due to
– rare disease
– short duration

19
Cohort Studies
• A cohort is a group with something in common e.g., an exposure
• Start with disease-free “at-risk” population
• i.e., susceptible to the disease of interest
• Determine eligibility and exposure status
• Follow-up and count incident events
• a.k.a prospective, follow-up, incidence or longitudinal
• Similar in many ways to the RCT except that exposures are
chosen by “nature” rather than by randomization

20
Types of Cohort Studies
• Population-based (one-sample)
• select entire popl (N) or known fraction of popl (n)
• p (Exposed) in population can be determined
• Multi-sample
• select subgroups with known exposures
– e.g., smokers and non-smokers
– e.g., coal miners and uranium miners
• p (Exposed) in population cannot be determined

21
Disease
+ -
Exp + a b n 1
Exp - c d n 0
N
Prospective Cohort Study – Population-
based Design (select entire pop)

22
Disease
+ -
Exp + a b n 1
Exp - c d n 0
Prospective Cohort Study – Multi-sample
Design (select specific exposure groups)
Rate= c/ n 0
Rate= a/ n 1

23
Exposed
Eligible
subjects
Unexposed
Disease
Disease
No disease
No disease
FUTURE
NOW

24
Relative Risk – Cohort Study
• RR = Incidence rate in exposed
Incidence rate in non-exposed
• The RR is the standard measure of association for cohort studies
• RR describes magnitude and direction of the association
• Incidence can be measured as the IDR or CIR
• RR = a / ni or a / (a + b)
c / no c / (c + d)

25
Example - Smoking and Myocardial Infarction (MI)
MI
+ -
Smk + 30 970 Rate = 30 / 1000
Smk - 10 990 Rate = 10 / 1000
RR= 3
Study: Desert island, pop= 2,000 people, smoking prevalence= 50%
Population-based cohort study. Followed for one year.
What is the risk of MI among smokers compared to non-smokers?

26
RR - Interpretation
• RR = 1.0
• indicates the rate (risk) of disease among exposed and non-
exposed (= referent category) are identical (= null value)
• RR = 2.0
• rate (risk) is twice as high in exposed versus non-exposed
• RR = 0.5
• rate (risk) in exposed is half that in non-exposed

27
RR - Interpretation
• RR = > 5.0 or < 0.2
• BIG
• RR = 2.0 – 5.0 or 0.5 – 0.2
• MODERATE
• RR = <2.0 or >0.5
• SMALL

28
Sources of Cohorts
• Geographically defined groups:
• Framingham, MA (sampled 6,500 of 28,000, 30-50 yrs of
age)
• Tecumseh, MI (8,641 persons, 88% of population)
• Special resource groups
• Medical plans e.g., Kaiser Permanente
• Medical professionals e.g.,
• Physicians Health Study, Nurses Health Study
• Veterans
• College graduates e.g., Harvard Alumni

29
Sources of Cohorts
• Special exposure groups
• Occupational exposures
– e.g., pb workers, U miners
– If everyone exposed then need an external cohort
non-exposed cohort for comparison purposes
– e.g., compare pb workers to car assembly workers
• Specific risk factor groups
– e.g., smokers, IV drug users, HIV+

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Cohort Design Options
• variation in timing of E and D measurement
Design Past
Prospective
Retrospective
Historical/pros.
E D
E E D
D
E
Present Future

31
Disease
+ -
Exp + a b n 1
Exp - c d n 0
Retrospective Cohort Study – Design
Go back and determine exposure status based on historical
information and then classify subjects according to their
current disease status
Rate= c/ n 0
Rate= a/ n 1

32
Exposed
Eligible
subjects
Unexposed
Disease
Disease
No disease
No disease
NOW
PAST
RECORDS

33
Examples retrospective cohort
• Aware of cases of fibromyalgia in women in a large
HMO. Go back and determine who had silicone
breast implants (past exposure). Compare incidence
of disease in exposed and non-exposed.
• Framingham study: use frozen blood bank to
determine baseline level of hs-CRP and then
measure incidence of CHD by risk groups (quartile)

34
PAR and PARF
• Important question for public health
• How much can we lower disease incidence if we intervene
to remove this risk factor?
• Want to know how much disease an exposure
causes in a population.
• PAR and PARF assume that the risk factor in
question is causal
• See Lecture 3 course notes

35
Venous thromboemolic disease (VTE) and oral contraceptives (OC) in woman of
reproductive age
• Incidence of VTE:
• OC users: 16 per 10,000 person-years
• non-OC users: 4 per 10,000 person-years
• Total population: 7 per 10,000 person-years
• RR = 16/4 = 4
• Prevalence of exposure to OC:
• 25% of woman of reproductive age

36
Population attributable risk (PAR)
• The incidence of disease in a population that is associated with
a risk factor.
• Calculated from the Attributable risk (or RD) and the prevalence
(P) of the risk factor in the population
• PAR = Attributable risk x P
• PAR = (16-4) x 0.25
• PAR = 3 per 10,000 person years
• Equals the excess incidence of VTE in the population due to OC
use

37
Population attributable risk fraction (PARF)
• The fraction of disease in a population that is attributed to a risk
factor.
• PARF = PAR/Total incidence
• PARF = 3/7per 10,000 person years
• PARF = 43%
• Represents the maximum potential impact on disease incidence
if risk factor was removed
• So, remove OC’s and incidence of VTE drops 43% in women of
repro age (assuming OC is a cause of VTE)

38
Exposed
Eligible
subjects
Unexposed
Disease
Disease
No disease
No disease
FUTURE
NOW
PARF = -

39
PARF Calculation
• PARF = P(RR-1)/ [1 + P(RR-1)]
where:
• P = prevalence, RR = relative risk
• PARF = 0.25(4-1)/ [1 + 0.25(4-1)]
• PARF = 43%
• Note that a factor with a small RR but a large P can
cause more disease in a population than a factor with
a big RR and a small P.

40
Selection Bias
• Selection bias can occur at the time the cohort is first
assembled:
• Patients assembled for the study differ in ways other than the
exposure under study and these factors may determine the
outcome
– e.g., Only the Uranium miners at highest risk of lung cancer (i.e.,
smokers, prior family history) agree to participate
• Selection bias can occur during the study
• e.g., differential loss to follow-up in exposed and un-exposed
groups (same issue as per RCT design)
• Loss to follow-up does not occur at random
• To some degree selection bias is almost inevitable.

41
Confounding Bias
• Confounding bias can occur in cohort studies
because the exposure of interest is not assigned at
random and other risk factors may be associated with
both the exposure and disease.
• Example: cohort study of lecture attendance
Attendance Exam success
Baseline epi proficiency
-
- +

42
Cohort Studies - Advantages
• Can measure disease incidence
• Can study the natural history
• Provides strong evidence of casual association between E
and D (time order is known)
• Provides information on time lag between E and D
• Multiple diseases can be examined
• Good choice if exposure is rare (assemble special exposure
cohort)
• Generally less susceptible to bias vs. CCS

43
Cohort Studies - Disadvantages
• Takes time, need large samples, expensive
• Complicated to implement and conduct
• Not useful for rare diseases/outcomes
• Problems of selection bias
• At start = assembling the cohort
• During study = loss to follow-up
• With prolonged time period:
• loss-to-follow up
• exposures change (misclassification)
• Confounding
• Exposures not assigned at random

44
Prognostic Studies - predicting outcomes in those with
disease
• Also measured using a cohort design (of
affected individuals)
• Factors that predict outcomes among those
with disease are called prognostic factors
and may be different from risk factors.
• Discussed further in Epi-547

EPI546_Lecture_8.ppt

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