Three main points to be covered

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Three main points to be covered

• Nature, weakness, and (sometime) strength of
  studies using group-level observations
• Cohort study as gold standard and its assumptions
  and limitations
• Concept of the study base linking case-control
  design to the cohort design

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Studies making observations on groups of
individuals vs. individuals

• Studies using group level data are usually called
  ecological studies
• Two main points about ecological studies
• Weak design for identifying cause and effect
  associations because of ecological fallacy
• In some study situations group-level measures may
  actually provide better inference than
  individual-level measures

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Example from Szklo and Nieto of grouped data from
cohorts in the Seven Countries Study
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Ecological Fallacy

• Cannot tell whether the predictor and the outcome
  are related at the individual level
• In this example cannot tell whether the
  individuals in the cohorts eating less saturated
  fat are the individuals who are experiencing a
  higher rate of heart disease
• Sometimes called confounding at the group level

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Confounding in group data

• If no ecological fallacy, still left with
  possible confounding some third variable really
  causing the increase in cancer and also related
  to number of births
• Difficult to control for because measures may not
  be available
• Even if data available, dont know relationship
  of confounding variable to other two variables at
  individual level

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Example of the potential strength of measures at
group level Effect of Floods in Bangladesh in
1988 on Children

• Children 2 - 9 years samples 6 months before
  flood and 5 months after
• Outcomes Enuresis and aggressive behavior
• Individual level predictor individual danger of
  drowning
• No association seen at individual level
• At group level, before and after flood comparison
  showed significant difference

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Situations where group level variables may be
better

• Exposures without much within group variability
  (salt consumption in U.S.)
• Herd immunity in studying infectious disease
  (vaccination levels may be more informative than
  individual behavior)
• Exposures that have powerful effects at group
  level (Bangladesh flood example)

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Conclusions on Ecological Studies

• As text emphasizes, common view that they are
  only hypothesis-generating is inadequate
• Weakest design for establishing causality but has
  a role because inexpensive and easy to do
• For some situations and kinds of data may
  actually be superior
• Some variables can only be measured at group
  level (policies and laws, environment)

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Cohort Study Design

• Gold standard because exposure/risk factor is
  observed before the outcome occurs
• Randomized trial is a cohort design in which the
  exposure is assigned rather than observed
• Other study designs can be understood by the way
  in which they sample the experience of a cohort

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Cohort study design
censored observations losses to follow-up
Minimum loss to follow-up (1)
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Time of Cohort Follow-up vs. Time when
measurements made

• Concurrent cohorts give most control because
  measurements are made at the same time as cohort
  assembly and follow-up (most texts call these
  prospective cohorts)
• Non-concurrent cohorts rely on obtaining
  measurements made in the past (most texts call
  these retrospective cohorts)
• Mixed cohorts obtain some measures made in the
  past and rest at same time as follow-up

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Selecting a non-concurrent cohort from a current
administrative data base

• Not a cohort study if you sample persons
  currently in the data base in order to insure
  retrospective data from past years
• cross-sectional sample
• no loss to follow-up by definition
• Must sample individuals from some baseline in the
  past in the data base
• ascertain outcome, losses to follow-up from that
  time forward

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Non-concurrent cohort study cannot be defined by
presence at end of follow-up
This is the cohort
Not the cohort
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Main Threat to Validity of a Cohort Study

• Subjects lost during follow-up
• Goal is to retain everyone but number of losses
  is less important than characteristics of those
  leaving
• How are losses related to outcome and risk factor?

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Subjects lost during follow-up

• If losses are random, only power is affected
• If disease incidence is important question,
  losses will bias results if related to outcome
• If association of risk factor to disease is
  focus, losses will bias results only if they are
  related to both outcome and the risk factor
• If losses introduce bias in the outcome, the
  censoring is called informative censoring

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Crucial issue is who is leaving cohort what bias
do the censored observations introduce?
censored observations losses to follow-up
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Case Control Design Concept of the Study Base

• Study Base the population that gave rise to the
  cases (Szklo and Nieto call it the reference
  population)
• Key concept that shows the link between
  case-control design and cohort design
• Case-control design using the study base concept
  is most easily understood in the setting of a
  cohort study

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Nested Case-Control Study within a Cohort Study
Study Base Cohort
Controls Sampled each time a Case is diagnosed
Incidence Density
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Nested Case-control Study

• In text example, 4 cases occur at 4 different
  points in time giving rise to 4 risk sets of
  cases and controls
• Controls for each case are selected at random in
  each risk set from cohort subjects under
  follow-up at the time
• It follows from the random selection, that a
  control can later become a case
• Results can be just as valid as using entire
  cohort gives unbiased estimate of rate ratio

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Definition of a Primary Study Base

• Primary Study Base population that gives rise
  to cases that can be defined before cases appear
  by a geographical area or some other identifiable
  entity like a health delivery system

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Examples of Primary Study Bases

• Residents of San Francisco during 2001
• Members of the Kaiser Permanente system in the
  Bay Area during 2001
• Military personnel stationed at California bases
  during 2001

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Example of Case-Control Incidence Density
Sampling in a Primary Study Base

• Use cancer registry covering San Francisco County
  to identify all new cases of glioma during a
  defined time period
• At time each new glioma case is reported,
  randomly sample two controls from current
  residents of San Francisco

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Incidence Density Sampling in a Primary Study
Base (e.g., San Francisco County)
Primary Study Base
New residents
Nested case-control in an open cohort with new
subjects entering
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Case-Control Incidence Density Sampling in a
Primary Study Base

• Same as nested case-control sampling in a cohort
  study with exception that in-migration of new
  persons requires one additional assumption
• Just as losses to the study base should not bias
  the results, additions to the study base should
  not introduce bias

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Primary vs. Secondary Base

• Main problem with a primary base is often
  ascertainment of all cases
• Main problem with a secondary base is the
  definition of the base

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Case-Based Case-Control Study The Secondary
Study Base

• Secondary Study Base population that gave rise
  to cases, identified after cases diagnosed those
  persons who would have been among the cases if
  they had developed the disease during the time
  period of study
• Start with a cases and then attempt to identify
  hypothetical cohort that gave rise to them

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Case-Based Case Control Studies and the Secondary
Study Base

• Source of cases is often one or more hospitals or
  other medical facilities
• Problem is identifying the population who would
  come to those institutions if they were diagnosed
  with the disease
• Careful consideration has to be given to factors
  causing someone to show up at that institution
  with that diagnosis

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Case-control study starting with a sample of
cases and identifying secondary study base
Secondary study base
Sampling can be incidence density just as in
primary study base
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Case-Based Case Control Studies

• Example glioma cases seen at UCSF
• Difficult because referrals come from many areas
• One possible control group might be UCSF patients
  with a different neurologic disease
• Patients from a similar tertiary referral clinic
  are another possible control group

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Text example of case-based case-control design
shows sampling prevalent controls
Secondary Study Base
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Cross-Sectional Study Design
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Case-based design using prevalent cases
essentially same as cross-sectional design
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Example of case-based design using prevalent cases

• Sampling glioma patients under treatment in a
  hospital during study period
• Poor survival so patients in treatment will
  over-represent those who live longest
• Nature of bias variable and not predictable

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Study base and case-control design

• Critical point of case-control design is that
  the cases need to consist of all, or a random
  sample, of subjects in the base experiencing the
  outcome and the controls need to consist of a
  sample of the base that can be used to estimate
  the exposure distribution in the base

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Summary Points

• Ecological studies weak in showing cause but have
  some valuable features
• Nature, not the size, of losses to follow-up
  crucial in cohort studies
• Key to case-control design is specifying and
  sampling the study base
• Case-control results can be as valid as cohort
  results if study properly designed and
  measurements made without bias

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Does Pregnancy Protect Against Ovarian
Cancer?(Beral, Fraser, and Chilvers, Lancet,
1978)
Compared changes in average number of children
vs. ovarian CA mortality rates over
time Average family size of women born in each
5-year interval between 1861 and 1931 in England
and the U.S. was compared to the ovarian CA
mortality rates (standardized) for women of those
5-year generations
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Beral et al., Lancet 1978
r - 0.97
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Strengthening Ecological Associations
withmultiple group-level comparisons Five
additional types of group data were used

• Across Countries Average family size in 20
  countries for women born around 1901 vs. ovarian
  CA mortality
• By marital status and social class Ovarian CA
  mortality rates among women 55-64 in England and
  Wales by marital status and social class
• By religion Incidence follows family size for
  Catholic, Protestant, and Jewish women in N.Y.
  state
• By ethnic group U.S. blacks and Am. Ind. vs.
  whites
• Among immigrants Rates changed with family size

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Ovarian Cancer versus average family size in 20
countries
Beral et al., Lancet 1978
r - 0.75
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Example of effect of losses to follow-up in a
cohort study 100 subjects, 30 with risk factor
(RF) and 70 without
1/3 (10/30) with RF develop disease within a
year 1/10 (7/70) without RF develop disease
within a year With no losses to follow-up in one
year Disease incidence 17/100 17 in one
year RR 10/30 / 7/70 3.33
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Example 100 subjects, 30 with risk factor (RF)
and 70 without
Losses to follow-up related to disease but not to
RF 9 of 30 (30) with RF and 10 of 70 (14)
without RF lost to follow-up in one year but
risk in each group remains 1/3 and 1/10 Disease
incidence 13/100 13 in one year Relative
Risk 7/21 / 6/60 3.33 Incidence is changed
but Relative Risk is not
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Example 100 subjects, 30 with risk factor (RF)
and 70 without
Losses to follow-up related to both RF and
disease 9 of 30 (30) with RF and 10 of 70
(14) without RF lost to follow-up in one year,
and risk in each group is changed. Risk with RF
is now 1/4 and without RF is 1/6. Disease
incidence 15/100 15 in one year Relative
Risk 5/21 / 10/60 1.43 Both Relative Risk
and Incidence are changed