x

1. Strengths
• Can assess multiple health outcomes (and do
not have to make binary as in case-control)
• Possible to examine multiple exposures
– May not be possible with double-cohort design (i.e.,
one cohort exposed, one cohort unexposed within the
study)
• Participants can move between exposure groups
over time (with incidence density as measure of
disease)

2. Strengths
• Exposures assessed before outcomes (for
prospective) ensuring temporality
• Direct measurement of disease rates, risks
• Capture changing exposures and outcomes in
time

3. Challenges
• Loss to follow-up
• Expense, feasibility and participant burden of
repeated data collection
• Time to conduct (for concurrent)
• Inefficient for rare outcome (rare in the study
population)
• Non-concurrent relies on availability and quality
of historical data

4. Challenges
• Have to assure assessment of outcomes is not
differential with respect to exposure
– Blinding may be possible
• Exposure classification may be challenging

5. Challenges
• Exposure(s) of interest must to have sufficient
variation in the population chosen
– Example: cannot study effect of lead paint exposure
on child development if all children in the population
live in housing built after 1978
– Example: NHS II established because women were
taking oral contraceptives from much younger ages
(compared to NHS I)

6. Challenges
• Loss to follow-up – one of biggest challenges for cohort
studies and trials
• May introduce bias in measure of association
– Bias = systematic difference between association observed and
causal effect
AR = 45 per 1000 AR = 29 per 1000

7. Challenges
• If loss of participants is associated with outcome,
measurement of rates/risks will be inaccurate (e.
g., more likely to lose people shortly before they
develop the outcome of interest)
• If loss of participants is also associated with
exposure will introduce bias (e.g., more likely to
lose people in the exposed group shortly before
they develop the outcome of interest)