A set of training materials for professionals working in intervention epidemiology, public health microbiology and infection control and hospital hygiene.
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Jean Claude Desenclos
Types of epidemiological studies
Reviewed by: Marta Valenciano and Arnold Bosman (oct
Many epidemiologists consider that the studies they
are conducting are measurement exercises. Simple studies include measurements
of disease frequency which may be expressed as risks, rates, prevalence or
odds. More advanced studies will aim at identifying the causes of diseases and the
effect of specific exposures on disease occurrence. This achieved by comparing disease
frequency between sub groups of a population. This comparison can be expressed
as a difference or a ratio. These studies are sometimes called analytical
studies and the comparison "effect measure".
Measuring effect in various study designs
As stated above a core function of epidemiologists is
to measure the causal effect of an exposure on the occurrence of a disease. To
measure a causal effect we would have ideally to compare occurrence of disease
in exposed persons to what would have happened in the same persons, at the same
time, in the absence of exposure. This is however theoretical since such two
measurements, in the same group of persons under study, are not feasible during
the same time period.
In order to approach this theoretical situation as
closely as possible, we will use as unexposed group a population similar to the
exposed group but for the exposure. In these two populations (or in 2 subsets
of the same population, exposed and unexposed), we will then measure and
compare disease occurrence.
To compare disease occurrence between exposed and unexposed
populations epidemiologists will have either to assign exposure or to observe
populations naturally exposed. Assignment of exposure is only ethically feasible
when exposure is potentially protective (treatment, vaccine, preventive
measures). Observation of accidental or naturally assigned exposures will allow
us to study the effect of potentially harmful exposures.
To measure the effect of exposure several types of epidemiological
studies are available.
In Romans' time, a cohort of legionnaires consisted of
a group of soldiers sharing the same military events for a certain period of
time. In epidemiology we consider that a cohort consists of people belonging to
the same population and sharing similar experience for a defined period of
Cohort studies involve the comparison of disease
incidence over time (risk or rate) between two subsets of a population (two
cohorts). One of the 2 cohorts is exposed to a certain characteristic
(exposure). The other is not. All other things being equal between the two
cohorts but for their exposure. In both cohorts we measure occurrence of
disease over the specific study period. However whenever the condition of "all
other things being equal" is not met, the comparison might be wrong.
The following graph adapted from Rodrigues illustrates
occurrence of cases over time in the two cohorts. Initially Ne persons are
exposed and Nu persons are unexposed. The number of persons who are disease
free decreases over time (shaded area). The number of cases (non shaded area)
increases over time but more in the exposed cohort. At the end of the study,
respectively Ce and Cu cases have occurred in the exposed and unexposed cohorts.
The shaded area represents the cumulative time during which persons were at
risk of developing disease in each of the cohorts during the entire follow up
from Rodrigues L et al. Int J Epidemiol. 1990;19:205-13.
Cohort studies measuring risk (incidence
Cohort studies that measure risk compare occurrence of
disease between exposed and unexposed cohorts. The risk (incidence proportion) of
disease in those exposed (IPe) and unexposed (IPu) can be computed as follows:
= number of cases among exposed / exposed population at risk at beginning of
the above example IPe = Ce/Ne
= number of cases among unexposed / unexposed population at risk at beginning
the above example IPu = Cu/Nu
The absolute effect of exposure on disease occurrence
is the risk difference (RD) between the exposed and unexposed cohorts.
effect (RD) = IPe - Ipu (Also called
« absolute risk reduction »)
The relative effect of the exposure on disease occurrence
can be expressed as the risk difference between exposed an unexposed, divided
by (relative to) the risk in unexposed.
Risk difference IPe-IPu
effect = ------------------------ = ------------ = --------
- ---------- = RR - 1
Risk in unexposed
IPu IPu IPu
Where RR is the risk ratio defined as:
Ratio = -------
of gastroenteritis according to consumption of food X, nursing home A
Consumption of food X Population
at risk Cases IP Risk
Ratio Relative effect
Yes 150 60 0.4 4 3
No 100 10 0.1
One can express the result by saying that the relative
effect of consuming food X is 3 which would suggest a 300% increased risk of gastroenteritis
among exposed. One can also express the results by saying that the risk of
disease is 4 times higher in the exposed cohort than in the unexposed cohort.
Thus the relative effect is the risk ratio minus 1.
Since the relative effect is RR - 1, epidemiologists frequently refer to RR as
a measure of relative effect without subtracting 1. The term "relative risk" is
very popular among epidemiologists even if, as mentioned above, it is not a
measure of relative effect but rather a risk ratio. When using the relative risk that way we have
to remember that a value of 1 corresponds to an absence of effect.
Cohort studies measuring incidence
The computation of effects with incidence rates is
similar to calculation of effects from incidence proportions (risk). The incidence rate of disease in exposed (IRe)
and unexposed (IRu) can be computed as follows:
= number of cases exposed / sum of person-time at risk among exposed population
= number of cases unexposed / sum of person-time at risk among unexposed
A rate difference can be computed: Rate
difference = IRe - IRu
The relative effect of the exposure on disease
occurrence can be measured by computing the rate ratio minus 1.
------- - 1
The rate ratio is:
ratio = -------
cancer cases and person-years of observation for women with tuberculosis
repeatedly exposed to multiple x-ray fluoroscopies and unexposed women with
Radiation Person-years *** Rate Rate ratio Rate Relative
exposure cancer /10000 p-y difference
Yes 28 010 41 14.6 1.86 6.7 0.86
No 19 017 15 7.9
Total 47 027 56 11;9
Boice & Monson; Rothman, Epidemiology, an introduction, p 50
One can express the result by saying that the relative
effect is 0.86 which would suggest an 86 % increased rate of *** cancer among
exposed. One can also express the results by saying that the rate of ***
cancer is 1.86 times higher in the exposed cohort than in the unexposed cohort.
Case control studies
In cohort studies the denominator represents the
exposure experience of the source population. If it is the exposure status as
observed at the beginning of the cohort we will compute a risk. If we allow for
exposure to vary overtime we will compute a rate which takes into account the
time spent by each individual in the exposed and unexposed cohorts over time.
The main constraint in cohort studies is the necessity
to collect information on exposure from large populations (to have denominators
for the exposed and unexposed cohorts). We will see below that instead of
collecting exposure information from the entire study population we can use a
sample of it to calculate or estimate the risk ratio or the rate ratio. In
other words, instead of using the entire cohort denominator we will use a
sample of it. This sample is also frequently called a control group and it is
used to represent the exposure experience of the source population.
The rationale behind using a sample of the denominator
comes from the following formula for risk and rate ratio which can
alternatively be expressed as follows:
RR = ---------- = ----------
RR = ------------- = -----------------
From the above formula we already see that if we take
an unbiased random sample of Ne and Nu the ratio of exposed to unexposed (Ne/Nu) will not be modified and
therefore the risk ratio will remain the same (Ne/Nu or a sample of it gives
the same risk ratio if sampling is unbiased). The same applies if we use person
years at risk (PT). The concept will be further explained below.
We have generally speaking three major ways to select
a sampled control group which reflect three ways to estimate exposure experience
in the source population.
1 - controls are randomly selected from the population present (at risk)
at the beginning of the study (Ne and Nu in the above graphic). The related
study design is called a case cohort study. The exposure measured reflects exposure
status at the beginning of the cohort.
2 - controls are selected proportionally to the person-time contributed
by exposed and unexposed cohorts (PTe and PTu). The related study design is
called a density case control study. The exposure measured reflects the varying
exposure of people at risk along the cohort.
3 - controls are selected from
people who are still free of the disease at the end of the study period (Ne-Ce
and Nu-Cu). We will call the related study design a traditional case control since
it is the design most frequently used. The exposure measured reflects the
exposure experience or status of people still free of disease at the end of the
Modify the order? And
start with traditional case-control studies?
Case cohort studies
In case cohort studies we aim to achieve the same goal
as in cohort studies but more efficiently using a sample of the denominators of
the exposed and unexposed cohorts. Properly conducted case cohort studies
provide information that should mirror what could have been learned from a
We will call "source population" the population which
gives rise to cases. The source population includes exposed and unexposed cohorts
and in that source population we could have conducted a cohort study comparing
risk or rates of disease between exposed and unexposed cohorts.
If instead we decide to do a case cohort study, we will
include the same cases, and classify them as exposed or unexposed. But, instead
of getting exposure information from all individuals constituting the
denominators of exposed and unexposed cohorts, we will only use a sample of them.
The purpose of this sample is to estimate the relative size of exposed and
unexposed components of the source population (the proportion of exposed in the
source population at the beginning of the cohort).
To do so we select a random sample from the entire
source population. If that sample is unbiased (sampling done independently from
exposure status) we expect (disregarding sampling variation) the distribution
of exposed and unexposed persons in the sample to reflect the exposure
distribution in the source population at the beginning of the cohort. This is
an important aspect of case cohort studies. The sample should be representative
of the population giving rise to cases (the source population) regarding
One way to imagine case cohort studies is therefore to
think of them as nested within cohorts of exposed and unexposed people. Any
case cohort study could be thought off as nested from the source population.
The sample group (control group) is a sample of the denominator present at the
beginning of the cohort.
From a cohort study measuring risk of disease in
exposed and unexposed cohorts we can draw the following results table:
Exposure cases Population at risk IP Risk
Yes a Ne a/Ne a/Ne / c/Nu
No b Nu c/Nu
If instead of studying the entire denominators of
exposed and unexposed we were sampling them (let's say 10%) we would have the
Exposure Cases Sample from source population
Yes a Ne/10
No b Nu/10
Obviously from the above table the risk of disease
cannot be computed since denominators sampled from exposed and unexposed cohorts
are only a sampling fraction of these two populations. However, if risk can no
longer be computed for exposed and unexposed, the risk ratio remains the same.
If in the risk ratio calculation we replace the denominators by the 10% samples
representing them, we obtain the same value for the risk ratio.
Ratio = ---------
When the sample is randomly selected from the source
population the risk ratio computed using the sample equals the risk ratio
computed within the entire cohorts.
Since we are randomly selecting controls from the
source population as it was at the beginning of the study (before disease
occurrence), it may happen that persons who will later become a case will be
selected as controls. Therefore some persons may appear both in the case and
control groups. This should not come as a surprise. In a cohort study cases are
counted in the numerator and denominators of exposed and unexposed. The same
applies to case cohort studies since we use a sample of exposed and unexposed
people of the source population. We are
not concerned by the disease status of the control group but by its exposure
status. The aim of the control group is to properly reflect the exposure in the
source population and this source population originally includes people who will
later become cases. Excluding future cases would lead to overestimating the
risk ratio, this particularly when disease occurrence is high.
Case cohort studies are a reasonable way to conduct
case control studies when disease if frequent, when people in the source
population have been followed for the same length of time or for short periods
of time, and when exposure does not change over time.
Density case control studies
In cohort studies, incidence rates are sometimes
called incidence density rates. By similarity we will call "density based
sampling" a sampling method in which the sample used as controls will represent
the person time experience of exposed (Pte) and unexposed (PTu) cohorts in the
source population. Thus the probability of any person from the source
population to be selected in the sample is proportional to his/her person-time
contribution to the denominators of the incidence rates in exposed and
Incidence (density) rates in exposed and unexposed
cohorts of the source population can be expressed as follows:
IRe = ----
IRu = ----
Where "a" is the number of cases exposed, "b" the
number of cases unexposed, "PTe" the total person-time accumulated by exposed
persons and "PTu" the total person-time accumulated by the unexposed group.
If instead of studying the entire denominators of person
time being exposed and unexposed we were sampling them (let's say 10%) we would
have the following table:
Yes a PTe/10
No b PTu/10
Obviously from the above table the incidence rate of
disease cannot be computed since person time denominators sampled from exposed
and unexposed are only a sampling fraction of these two populations. However,
if incidence rate can no longer be computed for exposed and unexposed, the rate
ratio remains the same. If in the rate ratio calculation we replace the person
time denominators by the 10% samples representing them, we obtain the same
value for the rate ratio.
Ratio = ---------
In this study design the sample (control group) is
randomly selected from the person time experience of the source population. As
a consequence the rate ratio computed using this sample is equal to the rate ratio
computed within the cohort study done with the entire person time denominators
of the source population.
The next issue is obviously about how to select a
sample and make sure it represents the person-time experience of the exposed
and unexposed cohorts in the source population. It is in fact quite simple. Each
time a case occurs, an individual (or several) is randomly selected from the
source population which is still free of the disease at the time of the case
onset. This is sometimes called prospective case control study. A mathematical explanation
of this rational can be found in Laura Rodrigues (reference).
For each person contributing time in the source
population experience, the time that this person is eligible to be selected in
the sample is the same time during which she is also eligible to become a case
if the disease should occur. Selecting an individual at the time of disease
onset in a case leads us to select a sample among people still at risk and
therefore proportionally to their time participation so far in the study. People
who have left, are dead or who are already cases cannot be selected from that
time on. This is also meaning that a selected individual who is still at risk of
disease can later become a case in the study.
Let us suppose that Boise and Monson had decided to do a density
case control study instead of a person-time cohort study. They would have
identified the 56 cases that occurred in the two cohorts and selected a sample series
of 470 women. The sample series group should be sampled so that the person time
distribution of the sample mirrors the person time distribution of the source
population. If randomly selected and unbiased, this would give us 280 exposed
and 190 unexposed in the sample (59.6 % of the sample is exposed which is equal
to the proportion of exposed person time in the source population, 28010 /
Cases and sample selected from *** cancer cases and
person-years of observation for women with tuberculosis repeatedly exposed to
multiple x-ray fluoroscopies and unexposed women with tuberculosis
Radiation Person-years *** Rate Rate ratio Sample Rate
/10000 p-y source sample
Yes 28 010 41 14.6 1.86 280 1.86
017 15 7.9 190
Total 47 027 56 11;9
The controls represent person years at risk experience
among exposed and unexposed. Controls are selected concurrently from those
still at risk when a case occur. A person selected as a control can later become
a case, the opposite is not possible since a case is no longer at risk of developing
disease (with a non recurrent disease). A control which later becomes a case is
kept in both groups.
Using density sampling allows us to compute a rate
ratio which is equal to the rate ratio we would have obtained if a cohort study
had been conducted to compare rates between exposed and unexposed cohorts in
the source population. Density case control studies require an analysis matched
on time of disease onset and control selection.
Traditional case control studies
The two above study designs used respectively as
denominators a sample of the exposed and unexposed source population or a
sample of their person time experience.
Let suppose that we now are at the end of the follow
up period and have respectively Ce and Cu cases, and Ne-Ce and Nu-Cu persons
still free of disease (non cases), in the two cohorts. If the disease is rare
it is clear from the graph that persons free of disease at the end of the study
period reflect the exposure experience of the source population. If the disease
is frequent, exposure among persons free of disease at the end of the study may
be lower than in the source population (since exposure increases the risk of
If the disease is rare, similarly to what we did in
the case cohort study, we can use a sample of non cases at the end of the study
period to estimate the risk ratio. Using non cases to estimate the source
population exposure experience is the principle of traditional case control
Let's call "c" and "d" respectively the number of
exposed and unexposed in the sample. If sampling is done independently from the
exposure status we would expect:
c Ne-Ce Ne
--- = --------- = ---- if the disease is rare
If the above is true the risk ratio estimated from a
traditional case control study can be represented as:
IPe a/Ne-Ce a Nu-Cu a d
------ = -- x ---- = -- x --
IPu b/Nu-Cu b Ne-Ce b c
The quantity ad/bc is the odds ratio. It represents
the ratio of the odds of disease among exposed divided by the odds of disease
However if the disease is not rare a large part of
Ne/Nu is represented by future cases who are more likely to be exposed than non
cases. Consequently, the odds ratio may dramatically overestimate the risk
--------- may not be equal to ------
To illustrate this point let's now move to the example
of a food borne outbreak in a nursing home with 200 residents and 74 cases of
gastroenteritis. The epidemic curve is consistent with a point common source of
infection and example 4 shows the results of a retrospective cohort study. It
suggests that the risk of gastroenteritis is 3.4 times higher among residents
who consumed a specific food item compared to those who did not (Ref.).
Example 4: Occurrence of gastroenteritis among
residents of nursing home A according to consumption of a specific food item.
Specific food item Total Cases Risk Risk ratio
Yes 60 44 73.3% 3.4
No 140 30 21.4% Reference
Total 200 74 37.5%
Let's suppose investigators would have preferred to
conduct a traditional case control study (case - non cases study) rather than a
Example 5: Consumption of a specific food item among
cases and various samples of residents of a nursing home
Consumption Cases 50 %sample OR 50%
non cases of
Yes 44 8 10.1
No 30 55 70
controls a 50% sample of the non cases the odds ratio equals 10.1,
overestimating the risk ratio by a factor of three. This should not come as a
surprise, though. When selecting controls from non-cases, and since the disease
is frequent (the overall risk of gastroenteritis is 37.5%), the control group
is no longer representing the distribution of exposure in the source population.
The frequency of exposure in the control group selected from non cases is 7.3%
and was 30% in the source population.
If instead we had done a case cohort study and chosen
a 50% random sample of the source population, the sample (if unbiased and
ignoring random variation) would be likely to provide the same proportion of
exposed (30%) than in the source population. The risk ratio obtained (3.4) would
again be similar to the risk ratio observed in the cohort study.
Cohort studies allow to directly measuring risk or
rate of disease occurrence and their related ratio in subgroups of a population
(exposed and unexposed). Case control studies do not allow measurement of risk
or rates. They however allow estimation of the risk ratio and the rate ratio.
The selection of the control group is a crucial step of the study. The
following table summarises the type of measures and controls selection as
described in the above chapter.
Measuring risk, rate and odds ratios in a case control
study, using various sampling methods for controls (Source:
Rodrigues L et al. Int J Epidemiol. 1990;19:205-13)
Measure Definition Case control Controls
Formulation Design sampled from
1 - Risk ratio Ce/Ne Ce/Cu Case cohort Total study population
Cu/Nu Ne/Nu present at
beginning of study
2 - Rate ratio Ce/PTe Ce/Cu Density People
(incidence Cu/PTu PTe/PTu case control at
time of case disease onset
3 - Odds ratio Ce /
(Ne-Ce) Ce/Cu _________ Traditional People disease free throughout
/ (Nu-Cu) (Ne-Ce) / (Nu-Cu) case control study period
Controls are randomly selected from the population present (at risk)
at the beginning of the study (Ne and Nu)
Case cohort studies are a reasonable way to conduct case control studies
when people in the source population have been followed for the same length of
Density case control
Controls are selected proportionally to the person-time contributed by
exposed and unexposed cohorts (PTe and PTu).
A matched analysis on
time of selection is necessary to give an unbiased estimate of the incidence
outbreak of hepatitis C in a dialysis unit where 3 controls per cases are
sampled among those at risk at the same time as the case occur
Traditional case control
Controls are selected from people who are free of the disease at the end
of the study period (Ne-Ce and Nu-Cu).
The OR is a good estimate of the risk ratio if the disease is rare.
These parallels in thinking between case-control and
cohort studies help to clarify the principles of control selection and
illustrate the importance of viewing case-control studies simply as cohort
studies with sampled denominators.
When facing difficulties in recruiting appropriate
controls in a case control study one should think at the cohort that could have
been done instead. Identifying the source population, the disease frequency and
the type of exposures will then guide the selection of a control group representative
of the exposure experience of the source population.
What design should be used and when?
Traditional case control studies are an easy and very
convenient way to conduct epidemiological studies when the disease is rare.
Because of its simplicity it is the most popular method. It has been extremely
useful to epidemiologists in the past 50 years. Provided that the disease is
rare the odds ratio provides a good estimate of the risk ratio. However it
should not be used when disease incidence is high. This particularly applies to
investigation of food borne outbreaks with very high incidence.
Case cohort studies are not very popular. Their
concept in not well understood to the point that some journals would reject a
case cohort study on the reason that the control group includes cases. Case
cohort studies are a very suitable design when disease incidence is high. They
provide a direct estimate of the risk ratio. They are not suited when exposure
changes over time (if exposure is measured
at the beginning of a follow up period and differs from the overall exposure
experience during the entire study period).
Density case control studies are suited for estimation
of rate ratios (incidence density rate ratios). They are simple to conduct. In
fact, to select a control among persons still free of disease, at the time a
case occurs, is common practice frequently called prospective case control
study. It provides a good estimate of the rate ratio. Density case control
studies are suited when unequal length of follow up occurs for study members.
Advantages and disadvantages of cohort and case
Many text books have described advantages and
disadvantages of cohort and case control studies. The following table
summarises usual comments.
Suited for rare diseases
Yes since starting wit a set of cases
Suited for rare exposures
Yes since starting with exposure status
Allows for studying several exposures
Difficult but examples exists
Allows for studying several outcomes
Disease status easy to ascertain
Easier since starting point of the study
Exposure status easier to ascertain
Yes since starting point of the study.
Except for retrospective cohorts
Allows computation of risk and rates
Allows computation of effect
Computation of risk ratio
and rate ratio
Estimation of risk ratio, rate ratio
from odds ratio
Allows studying natural history of disease
Easier to show that cause precedes effect.
Temporality between cause and effect difficult to
Based on existing data sources
Yes but access to information sometimes difficult
Easiness to find a reference group
Usually not difficult to identify an unexposed
Major potential biases when selecting a control
except if retrospective cohorts
Long, sometimes very long except if retrospective
Difficult, loss to follow up
No follow up
Many staff, large data sets
Easy to understand
Difficult to understand particularly if case cohort
or density case control study
Major if studying risk factors.
Interruption of study if exposure shown to be
Need for intermediate analysis.
None since outcome already happened.
Case cross over design
Among cohort designs, cross over studies are
intervention studies in which the same group of people is exposed to two
different interventions in two separate periods of time. This requires that the
effect of the intervention is short enough not to impact on the effect of the second
intervention and that a time gap between the two interventions is respected.
Case cross over studies are the case control version
of crossover studies. This concept was introduced by Maclure in 1991 (Am J
Epidemiol 1991;133:144-53). In a case cross over design all subjects are cases
and exposure is measured in two different periods of time. The general
principle is to find an answer to the question: "Was the case-patient doing
anything peculiar and unusual just before disease onset?" or "Did the patient
do anything unusual compared to his routine?". The assumption is that if there
are triggering events, these events should occur more frequently immediately
prior to disease onset than at any similar period distant from disease onset.
In case cross over studies, instead of obtaining
information from two groups (cases and controls), the exposure information is
obtained from the same case group but during two different periods of time. In
the first period exposure is measured immediately before disease onset. In the
second period exposure is measured at an earlier time (supposed to represent
background exposure in the same person). Exposure among cases just prior
disease onset is then compared to exposure among the same cases at an earlier
time. Each case and its matched control (himself) are therefore automatically
matched on many characteristics (age, sex, socio economic status, etc.).
To illustrate that point Maclure used the following
example. Let suppose we study the role of heavy physical activity in the
occurrence of myocardial infraction (MI). Using a case cross over design we
could document exposure to heavy physical activity among cases in the hour immediately
preceding MI. We would then document exposure to heavy physical activity among
those same cases at another earlier time.
The following figure illustrates periods of exposures
taken into account in a case cross over study.
Source: Adapted from Jean Claude Desenclos, InVS, France
In the above figure the period immediately before
onset is called the « current » period and the other period "the
reference period". The two periods are separated by a "wash out period" in
order to avoid that exposure in the reference period is mixed with exposure in the
current period. The reference period of exposure is used to reflect average
exposure experience among cases. Case 1 was unexposed in current period (just
prior to onset) and exposed in the reference period. Case 2 was exposed just
prior onset and unexposed in the reference period. Case 3 was exposed in both
periods and case 4 in
From the above we should consider that the same case
and its 2 periods of exposure constitute a matched pair. Cases 1 and 2 are
discordant pairs and cases 3 and 4 concordant. This is why with a case cross
over design a matched pair analysis is required. Only discordant matched pairs
will be used in the analysis (see chapter on matching for rational).
In addition some characteristics of exposure and
outcome are noteworthy.
Exposure should change over time in the same person and over short
period of time.
Exposure should not be changing in a systematic way over time. In the
example of physical activity let' suppose we have documented exposure in the
hour immediately before onset and that we have documented reference exposure two
days before at the same time. This would not be appropriate if physical
activity occurs in a systematic timing (every second day at the same time).
Exposure should have a short term effect. Duration of exposure effect
should be shorter than average time between two routine exposures in the same
individual. The effect of a first exposure should have stopped before the next
Induction time between exposure and outcome should be short.
Disease must have an abrupt onset. Case cross over are not appropriate
if the exact date/time of onset is not available or if abrupt onset does not
exist (some chronic disease).
Several reference time periods can be used to document average exposure
among cases. In that instance, an average of time being exposed is computed and
compared to exposure just prior disease onset. The efficiency of the case cross
over method increases with the number of reference periods included.
As in any case control study the capacity to properly document exposure
should be identical in the two periods of time. In case cross over designs
information biases are a sensitive issue.
Even if confounding is controlled since a case is its own control, within-person
confounding can occur. In the example of heavy physical activity and MI,
another factor (anger) may be linked both to exposure (heavy physical activity)
and outcome (MI).
Case cross over and food borne outbreaks.
Case cross over design was sometime used by
epidemiologists to try to identify a food item as the vehicle for a food borne
disease outbreak. Several of the above listed points merit to be challenged. A
recall (exposure) period of around three days may be too large to use this
design. In addition food habits (average exposure) do not happen randomly in an
individual. Finally, comparing consumption of a potentially infected food item
in the "current" period to average consumption of a similar un-infected food
item in the reference period does not relate to the same exposure. Consumption
of a food item could be identical in the current and reference time periods and
still only the food item in the current period was contaminated.
Case to case study design
Case to case studies are types of case control studies
used when the disease of interest can be sub-classified in two or several
groups that have specific risk factors. In a case to case study cases with a
particular sub-type of a disease are compared to cases with another subtype.
For example during a listeriosis outbreak, cases with the outbreak sub-type
would be compared to sporadic cases (the controls).
Some assumptions are made. Non outbreak cases (the
controls), if infected with the outbreak subtype would have been classified as
cases. They come from the same population which gave rise to outbreak cases.
They represent exposure (e.g. food consumption) in the source population for
outbreak cases. This is probably the major issue. Are sporadic cases of
listeriosis representing food consumption in the general population? This may
not always be true. Non epidemic cases may be more likely to be exposed than the
overall source population. We may therefore underestimate the odds ratio.
Some advantages lie with case to case studies. Cases
are readily available. Since all subjects in the study are sick there also may
be less differential recall between cases and controls.
Case to case studies may be a convenient design when
information is available for the sub class of cases used as controls. However,
as in any case control study, investigators need to be very cautious and verify
that exposure in the control group reflects accurately exposure in the source
population for cases.
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(Which refutes conclusions of the Suisa's article).
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Arnold Bosman posted on 7/5/2011 9:01:11 AM:
We could debate whether or not we are able to 'measure' a causal effect. In my view, what we measure are observations (counts) expressed in numbers, rates, risks for example.
The comparison is already a computation, resulting in an 'effect', which caj be consided an estmation of the effect in the population.
If the effect (e.g. measure of association) is causal or not, cannot be measured, not tested. It can merely be inferred.
marcelius atanga replied on 6/16/2015 10:01:13 AM:
I think even when we take a survey and count, at the end the numbers obtained are only estimates,
and so more important to me is to what extend should an estimate be consider useful, in fact what are tolerable error margins, given that no 2 situations are truely comparable?
Arnold Bosman replied on 6/16/2015 10:33:11 AM:
Perhaps we should open a philosophy wiki?
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