3. Overview
Cohort: a group of similar people followed through time
together
A cohort study follows participants through time to
calculate the rate at which new (incident) disease occurs
and to identify risk factors for the disease
4. Overview
All cohort studies have at least two measurement times:
•An initial survey that determines the baseline exposure
and disease status of all participants
•One or more follow-up assessments that determine how
many participants have developed a new (incident)
disease since the initial examination
5. FIGURE 12-2
Framework for a
Cohort Study
(The letters a, b, c, and d correspond to
the equation shown in Figure 12-8.)
6. Types of Cohort Studies
Cohort studies take many forms. For simplicity,
consider three main categories:
•Retrospective cohort
•Prospective cohort
•Longitudinal cohort
7. Retrospective & Prospective
Both retrospective cohort studies and prospective
cohort studies recruit participants based on their
exposure status
•One group of participants is recruited because they are
known to have had a particular exposure
•A second group is recruited because they are known
not to have been exposed
8. Retrospective & Prospective
• Retrospective cohort studies use baseline information
collected at some point in the past and follow the
cohort to another point in the past or to the present
• Prospective cohort studies collect baseline data about
exposures and outcomes in the present and follow
the cohort to some point in the future
9. FIGURE 12-3 Times of Baseline and
Follow-Up Data Collection for Cohort
Studies
10. Retrospective & Prospective
Recruiting based on exposure status makes retrospective
and prospective cohort studies the optimal study
approaches for uncommon exposures.
Because the goal of cohort studies is to examine
incident disease, retrospective and prospective cohort
studies must be able to demonstrate that the outcome of
interest was not present in any members of the cohort at
baseline.
11. Retrospective & Prospective
The members of the two comparison groups for both
prospective and retrospective of studies should be
similar except for their exposure status.
12. Retrospective & Prospective
Examples:
•Recruit industrial workers exposed to a certain
chemical and similar workers in a plant that does not
use the chemical
•Recruit children with high blood lead levels (indicating
environmental exposure to lead) and low blood lead
levels who attend the same elementary school
13. Longitudinal Studies
Longitudinal cohort studies recruit participants based on
their membership in a well-defined source population,
then follow them forward in time
•Individual participants are assessed at baseline for
several exposures and diseases.
•Then they are followed forward in time to determine
the incidence rate for one or more outcomes of interest.
14. Longitudinal Studies
Examples of populations for a longitudinal study:
•All the residents of one town
•A representative sample of members of one
professional organization
•A representative group of students recruited from the
same university
15. Longitudinal Studies
• In a longitudinal study with a fixed population, all
participants start the study at the same time and no
one is allowed to join later
• In a study with a dynamic population, participants are
recruited using rolling admission and replacement of
dropouts
– For dynamic populations, the time to follow up is
usually based on individual participants’ dates of
enrollment rather than on a fixed calendar date
17. Retention
For prospective and longitudinal studies, loss of
participants to follow-up before the end of the study
period is a major concern.
Researchers must develop strategies that minimize the
burden of participation and that maximize interest in
continuing to participate.
18. Information Bias
All participants must complete the same assessments of
exposure and disease at baseline and follow-up to
prevent the information bias that might result when
exposed participants are more thoroughly examined for
disease than unexposed participants.
19. Analysis: Incidence
Incidence rate = the number of new cases of disease in
a population during a specified period of time divided
by the total number of persons in the population who
were at risk during that period
Individuals who already have the disease of interest at
the start of the study period are not at risk of getting
new disease, so they are removed from the denominator
21. Analysis: Person-Years
• Some cohort studies use person-time as a
denominator rather than simply counting persons
• Person-time is a way of accounting for different
individuals in the study population being observed for
different lengths of time
• Example: Over 4 years in a dynamic study, 10
participants may contribute 33 person-years of
observation
23. Analysis: Attributable Risk
Excess risk = attributable risk (AR) = the absolute
difference in the incidence rate between the exposed
population and the unexposed population
•Example: If 10% of the unexposed and 15% of the
exposed became ill during the study period, then the
excess risk in the exposed was 15% – 10% = 5%
This number represents the additional risk of disease in
the exposed that can be attributed to the exposure
24. Analysis: AR%
Attributable risk percent (AR%) = the proportion of
incident cases among the exposed that are due to the
exposure
•Example: If 10% of the unexposed and 15% of the
exposed became ill during the study period, then the
AR% is 5% ÷ 15% = 33%
•One-third of the cases of disease in the exposed could
have been prevented if the exposure was removed
26. Analysis: RRs
Rate ratio (RR) = relative rate = risk ratio =
relative risk = ratio of the incidence rate among
the exposed to the incidence rate in the
unexposed
28. Analysis: RRs
• RR = 1: the incidence rate was the same in the
exposed and in the unexposed, so the exposure is not
associated with the disease
• RR > 1: then the incidence rate was higher in the
exposed than in the unexposed, so the exposure was
risky
• RR < 1: the incidence rate was lower in the exposed
than in the unexposed, so the exposure was protective
29. Analysis: RR & 95% CI
• If the entire 95% confidence interval is less than 1,
then the RR is statistically significant, and the
exposure is protective in the study population
• If the entire 95% confidence interval is greater than 1,
then the RR is statistically significant, and the
exposure is a risk factor for disease in the study
population
30. Analysis: RR & 95% CI
• 95% confidence interval (95% CI) overlaps RR = 1
– The lower end of the confidence interval is less
than 1, suggesting protection
– The higher end of the confidence interval is greater
than 1, suggesting risk
– Conclusion: The RR is not statistically significant,
and the exposure and disease are deemed to have
no association
