EPB PHC 6000 EPIDEMIOLOGY FALL, 1997

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Retrospective Cohort Study
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Review- Retrospective Cohort Study
Retrospective cohort study Investigator has
access to exposure data on a group of people.
The study sample is divided into exposed and
non-exposed groups. Both the exposures and
outcomes of interest have already occurred
(hence retrospective). The disease experience
of exposed and non- exposed groups is compared
(e.g. risk ratio or rate ratio).
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Review - Retrospective Cohort Study
Retrospective cohort study
Exposure Disease

?
?
Both exposure and disease have already occurred
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Retrospective Cohort Studies (Also called
historical studies)
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Design Features
Strengths Can study the effects of exposures
that no longer occur (e.g. discontinued medical
treatments.) Quicker and less costly than
prospective cohort studies. Particularly
efficient for study of rare exposures, especially
occupational and natural history exposures.
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Design Features
Strengths (cont.) Particularly efficient for
studying diseases with long latency
periods. Can examine multiple effects of single
exposure. Can yield information on multiple
exposures. May allow direct measurement of
incidence of disease in exposed and non-exposed
groups (hence, calculation of relative risk).
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Design Features
Limitations Not useful for study of emerging,
new exposures. Reliance on existing records or
subject recall may be less accurate and complete
than data collected prospectively (e.g. records
were not recorded for the hypothesis of
interest). Information on potential confounding
factors are often unavailable from existing
records.
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IMPORTANT CONCEPTS IN COHORT STUDIES
Rates Versus Risks Calculating Person Time
Estimating the Empirical Induction Period
Estimating Effect of Relevant Exposure
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Rates Versus Risks
In some instances, it is more desirable to
calculate and compare incidence rates, rather
than incidence proportions (risks). -- Recall,
cumulative incidence provides an estimate of the
probability (risk) that an individual will
develop a disease during a specified period of
time. -- Whereas, an incidence rate centers on
how fast new cases are occurring in a population.
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Cumulative Incidence (CI)
No. of new cases of disease during a given
period CI -------------------------------------
------------------------- Total population at
risk during the given period Example During a
1-year period, 10 out of 100
at risk persons develop the disease of
interest. 10 CI ----- 0.10
or 10.0 100
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Incidence Rate (IR)
No. of new cases of disease during a given
period IR -------------------------------------
------------------------- Total person-time
of observation Range 0 to Infinity
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Incidence Rate (IR)
What is person time?

• When we observe a group of individuals for a
  period of time in order to ascertain the
  DEVELOPMENT of an event.
• -The actual time each individual is observed
  will most likely vary.

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Person-Time

• Each subject contributes a specific person-time
  of observation (days, months, years) to the
  denominator

Person Follow-up Time on Study Person
Yrs. 1 lt-----------------------------------
--gt 2 2 lt---------------------------------
-----D 2 3 lt-----------------WD
1 4 lt-----------------------------------------
--------------gt 3 5 lt-------------------
------------------gt 2 1995 1996 1997 1998
Jan. Jan. Jan. Jan.
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Person-Time
Person Follow-up Time on Study Person
Yrs. 1 lt-----------------------------------
--gt 2 2 lt---------------------------------
-----D 2 3 lt-----------------WD
1 4 lt-----------------------------------------
--------------gt 3 5 lt-------------------
------------------gt 2 1995 1996 1997 1998
Jan. Jan. Jan. Jan.
Number of Cases 1 Person Years of
Observation 10 IR 1 case / 10 person years
of follow-up
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Rates Versus Risks
Question Among persons with acute leukemia,
does antibiotic treatment prevent or delay the
onset of gram-negative bacterial infections (as
measured by the presence of fever). --- 35
patients receive antibiotic treatment all 35
develop fever 260 person days of
follow-up --- 40 patients do not receive
antibiotic treatment all 40 develop
fever 210 person days of follow-up
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Rates Versus Risks
TreatmentYES CI 35 / 35 1.0 (100) IR 35
/ 260 0.1346 / person day TreatmentNO CI 40
/ 40 1.0 (100) IR 40 / 210 0.1905 /
person day Risk Ratio 1.0 / 1.0 1.0 Rate
Ratio 0.1346 / 0.1905 0.7066
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Rates Versus Risks
Risk Ratio 1.0 / 1.0 1.0 Rate Ratio 0.1346
/ 0.1905 0.7066 Although antibiotic treatment
did not prevent gram-negative bacterial
infections, the rate ratio of 0.7066 suggests
that it delays the onset of occurrence. In other
words, the risk of developing gram-negative
bacterial infection on a given day is lower in
those treated with antibiotics.
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Rates Versus Risks
In addition to being more informative on how fast
new cases of disease are developing, the rate
ratio can also be much more informative than the
risk ratio, depending on the exposure and disease
being measured, and characteristics of the study
cohort -- this is particularly true for
time-dependent exposures (exposures that change
over time).
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Definitions
Open or Dynamic Population Population in
which person-time experience can accrue from a
changing roster of individuals. Fixed
Cohort Exposure groups are defined at the
start of follow-up with no movement of
individuals between exposure groups (e.g.
clinical trial). Closed Cohort or
Population Fixed cohort with no loss to
follow-up.
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Estimating Relevant Exposure
When exposures are dynamic, it is important
take into account these changes as subjects are
followed. Example Suppose a cohort of
industrial workers are continuously exposed to a
hazardous agent over the course of their working
career. We wish to compare the mortality
experience of those with low, moderate, and high
exposure to mortality in the general population
(see handouts).
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Estimating Relevant Exposure
Whether implicit or explicit, and whether for
cohort studies or case-control studies, it is
important to consider the empirical induction
period when estimating the effects of exposures.
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Empirical Induction Period
The empirical induction period includes the
time from causal action of the exposure to
disease detection. This consists of 2
parts Induction period Period of time from
causal action to disease initiation
(triggering). Latent period Time interval
between disease occurrence and detection.
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Empirical Induction Period
Example Smoking and lung cancer.
Years of smoking
0 30 40 45
Induction period
Latent period
Pre-causal period
Total study period 45 years Empirical
induction period 15 years Induction period 10
years Latent period 5 years
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Empirical Induction Period
For many exposure/disease associations, the
empirical induction period is unknown. The
latent period can be reduced by improved methods
of disease detection. Slow-growing cancers may
appear to have long induction periods with
respect to some causes because they have long
latent periods.
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Estimating Relevant Exposure
Depending on the empirical induction period, it
is often inappropriate to uniformly assign
persons as exposed or non-exposed. Instead,
persons can contribute person time to both
exposed and non-exposed denominators. In other
words, the time at risk of disease may vary
depending on levels of the accumulation and
intensity of exposure.
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Example Agent Orange exposure and thyroid
cancer. Exposed Combat veterans exposed to
Agent Orange in Vietnam (1967-70). Nonexposed
Veterans in non-combat positions not exposed to
Agent Orange in Vietnam (1967-70). Follow-up
Period 1970 - 2000 Postulated Empirical
Induction Period 20 to 30 years
Range of Empirical Induction Period
Range of Exposure
Range of Pre-Causal Period
1967 1970 1987 1990 1997
2000
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Estimating Relevant Exposure
If we give credit to person time during the
pre-causal period when exposed persons were
presumed not at risk of disease occurrence, we
may get a biased (usually diluted) estimate of
the relative risk (see handout).
Range of Empirical Induction Period
Range of Exposure
Range of Pre-Causal Period
1967 1970 1987 1990 1997
2000
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Estimating Relevant Exposure
An important issue is what happens to the time
experienced by exposed subjects that does not
meet the definition of time at risk of exposure
effects (the empirical induction period). The
non-relevant follow-up time can be 1. Assigned
to the denominator of the unexposed
rate. 2. Excluded from the study.
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Estimating Relevant Exposure
Advantages of assigning the non-relevant
follow-up time to the denominator of the
unexposed rate Greater precision in
estimating the rate among the unexposed. Greate
r comparability between the exposed and
non-exposed on characteristics such as age and
time period of follow-up.
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Estimating Relevant Exposure
Disadvantage of assigning the non-relevant
follow-up time to the denominator of the
unexposed rate If the empirical induction
period is underestimated, truly exposed cases
will be added to the rate of the non-exposed
this will tend to make the exposed and unexposed
rates more similar than they really are.
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Estimating Relevant Exposure
Disadvantage of excluding the non-relevant
follow-up time to the denominator of the
unexposed rate The number of truly unexposed
cases may be too small to produce a stable
comparison.
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Estimating Relevant Exposure
Since the empirical induction period is often
unknown, how do we know if it is
appropriate? The empirical induction period
can be lagged with separate analyses conducted
using each period -- e.g. 10 to 20 years 15 to
25 years 20 to 30 years 25 to 35 years 30 to
40 years 35 to 45 years
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Estimating Relevant Exposure
If multiple empirical induction periods are
analyzed One can select the largest risk
estimate, with the tenuous assumption that it is
not the largest simply due to statistical
variability (chance). The data should be
inspected to see whether a consistent pattern of
effects emerges that reflect the empirical
induction period.
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Timing of Outcome Events
To accurately estimate person time of follow-up,
it is important to determine the time of the
event as precisely as possible. Example
Defining the onset of time for disorders such as
multiple sclerosis and atherosclerosis can be
ambiguous.
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Timing of Outcome Events
As a general rule, there should be a written
protocol on how to classify subjects on the basis
of available information. Example
Seroconversion to HIV might be measured as the
midpoint between time of last negative and first
negative antibody test.