Cohort studies

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Cohort studies

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Title: Cohort studies

1
Cohort studies

Design Concepts in Nutritional Epidemiology
Barrie M. Margetts and Michael Nelson

Contents
General considerations
Practical issues
Infancy and childhood
Adults
The elderly
Concluding remarks
Appendix
Checklist for planning a cohort study
Analysis of cohort studies

3
General considerations

A cohort
A group of persons, identified at one
point in time, who march off together into the
future under the watchful eye of an investigator.
A cohort study
A group of persons is defined, certain
characteristics about each individual are
recorded, and they are then followed up in such a
way that new events (such as disease and death)
or other changes in their characteristics are
detected.
These new events and changes can then be
related to the original observations in order to
discover what aspects of the initial status of
the subjects predict their subsequent experience.

Two main types of cohort study
Prospective (more usual type)
--baseline information is collected
when subjects enter the study and they are
followed up over its duration
Retrospective
--- a 'historical cohort' is identified
with reference to some point in the past
--- This approach is possible only
where adequate records exist it has the
advantage that the effects of a long period of
time can be observed during a relatively short
period of study.

5
The use of the cohort study

In the elucidation of aetiology.
In studying the natural history of a disease, and
how this is modified by treatment.

6
Reasons for preferring the cohort type of study
to the case-control approach

It enables the investigator to obtain accurate
information about the individuals before the
onset of the disease being investigated.
Information obtained prospectively is not only
more accurate but is also less open to bias than
that obtained retrospectively, when the outcome
is known.
It allows us to detect unexpected effects of the
initial factors,whereas a case-control analysis
is restricted to the selected condition.
The findings show the strength of an association
in public health terms, so that the 'population
attributable risk' can be calculated.

7
The disadvantages of the cohort study

Prospective aetiological studies are inevitably
large, long, and expensive, even for the more
common diseases, and not usually feasible for
less common diseases.
It may happen that the subjects alter their
eating habits, either spontaneously or as a
result of changes in dietary fashion, to such an
extent that the original observations are
irrelevant.
The difficulties of tracing all the subjects,
unforeseen changes in the personnel involved in
the study, and the ultimate hazard that the
hypothesis being tested becomes superseded, so
that the investigator finds that the wrong
baseline.information has been recorded.

Occasionally, a 'nested' design is used,
combining the cohort and case-control approaches.
The sensitivity of the comparison is not much
less than it would be if the specimens from all
the subjects were used, and the expense of
analysis is greatly reduced. The advantages of
the cohort methods are thus retained, together
with the greater efficiency of the case-control
design.
Ex. If a large cohort is being followed up,
and a sample of every member's serum as been
stored, a case-control analysis can be made
within the cohort at a later date. Persons who
acquire a given disease are identified, and
controls (usually two or three per case) are
selected from the cohort, each control being
matched with a case in respect of age and gender.
The sera of cases and controls are extracted and
appropriate biochemical analyses are made.

9
Practical issues

To obtain expert statistical advice at the
planning stage so as to ensure that the study is
likely to be large and long enough but not
greatly in excess of the requirements.
Calculations of size and duration will be based
on the expected numbers of endpoints in the study
(see appendix 14.1).
Suitable forms are designed on which information
can be collected and coded for easy
computerization.
To conduct a short pilot study to test the
procedures that will be used in the survey.
Information can be obtained from a sub-sample and
will provide some estimate of the stability of
the original measurements in the group being
studied.

10
Practical issues

To acquire information during the whole of the
follow-up period, contact will then have to be
made with the subject periodically, either by
visiting or by telephone. If day-to-day
information is required (e.g. on infant feeding
methods) a diary may be used.
Some of the subjects will have moved or died, and
information may not be easily obtained,
particularly in a free-living population. Plans
should be made at the start of the study about
how the subjects will be traced.
It may be advisable to have periodic contact with
all the subjects (say at annual intervals), by
telephone or reply-paid cards, simply in order to
detect those who have left the area.

Another way of minimizing the difficulties of
finding subjects is to select them from certain
occupations whose members are particularly easy
to trace.
Ex. Medical practitioners ,Civil Servants
The main drawback of these groups is that
they do not entirely represent the population at
large, but are drawn from specific social
classes. Furthermore, people who are employed or
who volunteer always tend to be healthier than
others of their age-group , so that the cohort
may need to be larger. And the results may not
apply exactly to the wider population.
If the intention is to look for changes in
biochemical variates (e.g. serum cholesterol),
the study should be discussed with the biochemist
at the start so as to avoid a change in
laboratory methodology, which will invalidate
comparisons.

Changes in staff or collaborators may be more
difficult to foresee and can have a disastrous
effect if the newcomer does not have the same
degree of interest or commitment as the person
with whom the study was set up.
If the final measurements (e.g. of blood
pressure) are to be made by someone other than
the person who made the initial measurements,
attention should be paid to standardization and
comparability.
It may also be desirable for the person recording
the final measurements or endpoints to be unaware
of the initial data, so as to avoid bias.

13
Infancy and childhood

Infancy provides perhaps the best possible
opportunity for cohort studies.
A cohort of babies can easily be defined in terms
of place and time of birth. Some information
(e.g. regarding birthweight and initial mode of
feeding) is collected routinely and can be
supplemented with other details as required. The
duration of such studies can in principle be
extended indefinitely even the longevity of the
original investigator need not be a limitation!
Large national birth cohorts
The National Survey of Health and
Development, the National Development Study, and
the Child Health and Education Study
It is clear that early influences have long-term
predictive effects. It is difficult to rule out
potential confounding variables (e.g. long-acting
adverse environmental factors or genetic
influences.

14
Adults

Cohort studies in adults have been very useful in
elucidating the role of diet in the causation of
disease. Nutrition is a major determinant of
health, so it is obviously important to
investigate its influence on morbidity and
mortality. Everybody eats food, but not
necessarily the same food, so there are ample
opportunities for comparing the effects of
different dietary habits within the same
population. Ischaemic heart disease (IHD) is a
good example of a relationship between diet and
health, and has been investigated in this way.
Table 14.1 summarizes nine cohort studies that
have reported associations between diet and heart
disease.

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Cohort studies have yielded useful information
about nutritional factors in relation to other
common diseases
--The Framingham study indicated a
protective effect of fruit and vegetables against
stroke.
--Breast cancer has been positively
associated with alcohol intake and negatively
with vitamin A.
--In the Prospective Basel Study, plasma
anti-oxidants (carotene and vitamins A, C, and E)
were measured in 2974 Swiss men in the following
12 years, lung cancer was associated with
initially low plasma carotene and stomach cancer
with low vitamin C and lipid-adjusted vitamin A
levels.

17
The elderly

Reasons for expecting cohort studies to be
particularly easy and
fruitful in the elderly
--- The diet of an old person is likely to be
more uniform.
--- Nutritional deficiency is more common in old
age than at other
periods of life, yet many elderly people eat
very well. The dietary
variation between individuals is therefore
large in comparison
with the variation within individuals .
--- Morbidity and mortality are very high in the
elderly, so more
endpoints will occur per person-year of
follow-up than at younger
ages.

The confounding effect of differential ageing is
particularly important in old age,and is well
illustrated by the paradoxical finding that
obesity is a favourable prognostic index in the
elderly. Over the age of 65, people tend on
average to lose weight as they grow older. But
not everybody ages at the same rate. Some tend to
be leaner and to die earlier, while those who
arebiologically young for their chronological age
are on average fatter and less likely to die in
the next few years. But it does not necessarily
follow that thin old people would live longer if
they managed to put on weight.

19
Conclusion remarks

Cohort studies allow more rigorous testing of
aetiological hypotheses than other observational
studies.
They also provide unique information about the
natural history of disease.
Their disadvantages firstly concern feasibility,
because they tend to be large,long, and suitable
only for the study of common diseases. Secondly,
they share other weaknesses of the observational
approach, in that subjects who choose to eat one
type of diet probably differ from people who eat
a different diet in other ways that could affect
their risk of disease. This confounding is
particularly important when the nutritional
variables are likely to be associated with
particular lifestyles or the initial state of
health and senescence of the subjects.
Insofar as it is not possible to conduct
long-term, randomized controlled trails of
dietary changes in free-living populations,cohort
studies provide the best available evidence of
aetiology.

20
Appendix I Checklist for planning a cohort study

1. Purpose of the study
2. Value of the study
3. Definition of the cohort
4. Numbers
5. Recruitment of the subjects
6. Baseline data
7. Tracing of subjects
8. Collection of follow-up data
9. Analysis of data
10. General considerations

1. Purpose of the study
(a) What hypotheses will the study examine?
(b) What other specific questions will it
address?
2. Value of the study
(a) If the hypotheses are confirmed, will we be
any better off ?
(e.g. in our ability to understand disease
or treat patients)
(b) If the hypotheses are not confirmed, will
other scientists be interested?

3. Definition of the cohort
(a) Is the cohort to be identified
retrospectively or prospectively?
(b) What are the inclusion criteria (age, gender,
area of residence, etc.)?
(c) What exclusion criteria apply ?
(e.g. presence of certain diseases,
residence in institutions)
(d) If the cohort is recruited over a period of
time, at what point do the subjects
have to meet the age and other criteria
(e.g. at the start of the study, or when
the subjects are seen)?
(e) Are there any ambiguities in the way the
criteria are defined or recorded?
(f) Will the cohort comprise a total population
defined as above, or will it be a sample
of the population, and, if so, how will the
sample be selected ?
(e.g. randomly or by volunteering)

4.Numbers
(a) How large will the cohort be and how has its
size been calculated
(expected differences, statistical
power,etc.)?
(b) What allowances have been made for
non-response and migration of subjects?
5. Recruitment of the subjects
(a) How will the subjects be identified?
(b) How accurate and up-to-date is the sampling?
(c) Over what period will recruitment continue?
(d) Can we foresee any biases arising during
recruitment ?
(e.g. from selective identification or
response of subjects)

6. Baseline data
(a) What data are to be collected at baseline
(including potential aetiological
factors and possible confounders)?
(b) What checks could be conducted on
reproducibility, validity, comprehensibility
of questionnaires, etc., so that the
findings will be accepted as true?
(c) How soon can the data be checked, coded, and
computerized so as to allow early
detection and correction of errors and
omissions?
(d) If blood is taken, should specimens of
serum/plasma be kept deep-frozen for
future analysis in case further hypotheses
are suggested?

7. Tracing of subjects
(a) How will the subjects be traced and when?
(b) What secondary methods of tracing are
available for subjects who cannot be
traced by the primary methods?
(c) What biases are likely to arise from
incomplete tracing?
8. Collection of follow-up data (next page)
9. Analysis of data
(a) Is a statistician (preferably the person who
will undertake the analysis)
involved in the design of the study?
(b) What analyses of the data will be performed?
10. General considerations

8. Collection of follow-up data
(a) After what interval(s) will follow-up data be
collected?
(b) What information will be required
(repeat baseline data, outcome events, new
tests)?
(c) What checks should be made on the quality of
the data to be collected
(e.g. reproducibility, validity,
comparability with the baseline data)?
(d) Can we ensure that the outcome events are
recorded 'blind' with regard to the
initial observations?
(e) If some subjects are not available (e.g.
through migration or refusal), is there
any useful information that we can obtain
about them?
9. Analysis of data

10. General considerations
(a) What ethical issues arise (e.g. concerning
explanation and information
given to subjects signed consent forms for
tests and follow-up procedures)?
(b) What issues of professional etiquette must be
considered (e.g. whose
permission needs to be obtained who
should be informed as a courtesy)?
(c) If the data collection could disclose
abnormalities in the subjects (e.g. a high
serum cholesterol), what is our criterion of
abnormality and what do we do
when we find it?
(d) What are the costs of the study and what
personnel will be required ?
(next page)

(e) Is this the best time to start the study, or
would it be better to wait
(e.g. until the relevant technology has
improved)?
(f) Should the methodology of the study be made
comparable to that of any
other study (e.g. by using similar
questionnaires)?
(g) What experts should be consulted to increase
the likelihood that the findings
will be accepted as conclusive?
(h) Is somebody keeping a list of all the people
we promise to inform about the
conclusions of the study?

29
Appendix II Analysis of cohort studies (Clive
Osmond)

Cohort studies may be classified
according to both the
type of data that are collected at baseline and
the nature of the
eventual outcome measure. The combination
determines the
appropriate strategy for analysis. Below we
consider four
common combinations, mention the usual method of
analysis,
and give an example of each. (Table 14.2)

Design I
Measurements are made on individuals.
The outcome is the time to an event. The time may
be censored (that is, the event is known not to
have occurred up to the time specified). The
usual method of analysis is by the Cox
proportional hazards model. The effect size is
measured by the hazard ratio, rather similar to a
relative risk.
Example
Gale et al studied 730 elderly men
and women who had completed a 7-day dietary
record in 1973. They followed up the cohort for
20 years, noting when subjects died from stroke
or other causes. Allowing for age, gender, and
known cardiovascular risk factors, those who were
in the highest third of vitamin C intake had a
relative risk of 0.5 for stroke (95 confidence
interval 0.3-0.8) compared with those in the
lowest third. A similar gradient in risk was
present for plasma ascorbic acid concentrations.

Design 2
Measurements are made on individuals.
The outcome is a notionally continuous
measurement. The usual method of analysis is by
multiple linear regression and analysis of
covariance. The effect size is measured by the
regression coefficient. This assesses the change
in the outcome variable for a unit change in a
predictor variable. Thus when a predictor
variable is binary the regression coefficient
describes the contrast between two groups.
Example
Lucas et al. studied 502 pre-term
babies who were randomized to receive one of two
different diets during their early weeks - mature
donor breast milk or pre-term formula.At age 18
months the survivors were given mental
development assessments. A development score with
mean close to 100 (standard deviation about 20)
was obtained from each child. Regression
adjustment was made for gender, gestational age,
and social class. No clear difference in mental
development was found between the two feeding
groups.

Design 3
Measurements are made on individuals.
The outcome is a binary 'yes/no' variable. The
usual method of analysis is by multiple logistic
regression. The effect size is measured by the
regression coefficient, and this can be
transformed into an odds ratio.
Example
Richardson and Baird studied the milk
intake and calcium supplement use of a cohort of
9291 pregnant women in California. 268 women
experienced pre-eclampsia, the 'yes/no' outcome
variable in the study. Allowing for possible
confounders such as number of previous
pregnancies and body mass index, the odds ratio
for pre-eclampsia was 1.9(95 confidence interval
1.2-2.9) in those who drank less than one glass
of milk a day relative to those who drank two
glasses of milk a day. The odds ratio was also
higher(1.8 95 Cl 1.1-3.0) in those who drank
four or more glasses a day, again using as the
comparison group the two glass drinkers. The
authors interpreted the association of low levels
of milk consumption with pre-eclampsia as
consistent with data on calcium and hypertension.
The association of high milk consumption with
pre-eclampsia was unexpected. The authors
suggested that it needed to be replicated.

Design 4
Comparisons are made at a group
level. The outcome is a survival time (possibly
censored). This is known at the individual level.
Thus rates can be calculated for the groups.
Age and gender standardization of
rates is necessary. The analysis can be with an
internal comparison group, when Poisson rates
models are useful. An external comparison group,
often implied by the use of national
cause-specific mortality rates, leads to
standardized mortality ratios as the measures of
effect size. These are often scaled so that 100
corresponds to the rates in the external standard
population.
Example
Thorogood et al studied 6115 members
of the United Kingdom Vegetarian Society and 5015
of their meat-eating friends and relatives. The
subjects were followed up for 12 years.
Standardized mortality ratios (England and Wales
100) for ischaemic heart disease were 51 (95
confidence interval 38-66) for the meat-eaters
and 28 (20-38) for the vegetarians. Figures for
all cancer were 80 (64-98) and 50 (39-62) for
meat-eaters and vegetarians, respectively.

More complicated designs
If repeated measurements are made on
members of the cohort on different occasions,then
it is inefficient merely to average the data and
incorrect to regard all the observations as
statistically independent. More appropriate
models are needed, and it will almost certainly
be necessary to seek statistical support. Indeed
even the simpler designs carry their own
subtleties, making it wise to work routinely in
collaboration with a statistician.

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Homework