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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

2
  • 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.

4
  • 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.

8
  • 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.

11
  • 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.

12
  • 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.

15
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16
  • 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.

18
  • 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

21
  • 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?

22
  • 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)

23
  • 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)

24
  • 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?

25
  • 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

26
  • 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

27
  • 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)

28
  • (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)

30
  • 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.

31
  • 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.
  •  
  •  
  •  
  •  

32
  • 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.

33
  • 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.

34
  • 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.

35
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36
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