Cohort and case-control methodologies are the main tools for analytical epidemiological research. Other important types of epidemiological studies mainly for generating hypotheses include cross-sectional and ecological, or correlation studies. The conclusions that can be drawn from findings of these types of studies are, however, much weaker compared to those of cohort and CCSs. This is not to say that findings from cohort and CCSs always reflect true associations which can be universally generalized. Epidemiological research is, to a large extent, of an observational character as opposed to experimental research.
The study discussed in this thesis is known as a CCS. The cases’ history of exposure or other characteristics, or both, prior to onset of the disease, is recorded by interview and sometimes referring to records and other sources. A comparison group consisting of individuals without the disease under study but similar in other pre-specified criteria (controls) are selected, and their past history is recorded in the same fashion as for the cases. The purpose of the control group is to provide an estimate of the frequency and amount of exposure in subjects in the population without the disease being studied. This means that whereas cohort studies are concerned with frequency of disease in exposed and non-exposed individuals, CCSs are concerned with the frequency and amount of exposure in subjects with a specific disease (cases) and people without the disease (controls).
3.1.1 Measure of association
In CCSs, data are not available to calculate the incidence rate of the disease being studied, and the actual RR cannot be determined.
The measure of association between exposure and occurrence of disease in CCSs is the so-called odds ratio (OR): the ratio of odds of exposure in diseased subjects to the odds of exposure in the non-diseased. The following table exemplifies the basic method of calculating the OR in a CCS.
Table 3.1: Basis of calculating ORs in CCSs
Disease Exposure
Yes (cases) No (controls)
Yes a b
No c d
Odds of exposure a/c b/d
The OR is thus given by ] / [
] [
d b
c
a (or ad/bc). The OR is generally a good estimate of the
relative risk. The terms OR and RR are in fact interchangeable when used in CCSs.
3.1.2 Confounding and bias
As mentioned earlier, CCSs are observational studies and are potentially subject to the effect of extraneous factors which may distort the findings of these studies. The term confounding (or confounding factor) used in this context refers to an extraneous variable that satisfies both of two conditions: it is a risk factor for the disease being studied, and it is associated with the exposure being studied but is not a consequence of exposure1.
1 Schlesselman JJ, 1992
Adjusting for the effects of confounding factors is evidently important in observational epidemiological studies, and can be dealt with in the study design by matching or stratifying sampling of study subjects, or in the data analysis by stratification or multivariate analyses12 3.
Another potential complicating factor of not only observational but practically all types of research, is bias. Bias has been defined as any systematic error in the design, conduct, or analysis of a study that results in a mistaken estimate of an exposure’s effect on the risk of disease1. Sackett4 has provided an extensive discussion of various types of bias.
One type of bias frequently referred to in epidemiological research is “recall bias”, namely the propensity of diseased subjects (cases) when interviewed, to scrutinize their memory and report more accurately on past exposure and possible causes of their disease than non-diseased subjects (controls) would do. Such recall bias has been documented567.
1 Schlesselman JJ, 1992
2 Kleinbaum et al, 1982
3 Rothman KJ, 1986
4 Sackett DL, 1979
5 Hogue CJ, 1975
6 Klemetti et al, 1967
7 Lindefors-Harris et al, 1991
3.1.3 Advantages and disadvantages of CCSs
When faced with a research question concerning the association between a possible etiologic factor and disease, the epidemiologist has to choose an appropriate strategy to resolve the matter. A number of circumstances have to be considered before a certain type of research is chosen, such as the incidence rate of disease, time elapsing between exposure and clinical manifestation of the disease, whether the exposure is associated with only one or more diseases, the urgency of the research question, ethical issues, and funding available for the research, etc. Below are listed some of the advantages and disadvantages of CCSs.
Table 3.2: Advantages and Disadvantages of CCSs
Advantages Disadvantages
J Permit the study of rare diseases L Information on exposure and past history is primarily based on interview and may be subject to recall bias
J Permit the study of diseases with long latency between exposure and manifestation
L Validation of information on exposure is difficult, or incomplete, or even impossible J Can be launched and conducted over
relatively short time periods
L By definition, concerned with one disease only
J Relatively inexpensive as compared to cohort studies
L Generally incomplete control of extraneous variables
J Can study multiple potential causes of disease
L Choice of appropriate control group may be difficult
L Correct interpretation of results may be difficult
3.1.4 Assessment of causality
One of the more difficult tasks in epidemiological research is to assess whether associations between exposure and disease derived from observational epidemiological studies are of a causal nature or not. It has been underlined above that observational epidemiological studies are subject to the influence of factors over which the investigators most often do not have full control, and that findings from these studies are less reliable than those of studies with an experimental research design. It is therefore imperative that findings from analytical epidemiological studies are critically scrutinized before any judgment of causality is made. Furthermore, findings from one single epidemiological study only exceptionally provide conclusive evidence of a causal relationship between exposure and disease. Discussions and reasoning concerned with which criteria to apply for the assessment of causality have been given by several authors12 3.
Bradford Hill2 has listed nine aspects concerned with the association between exposure and disease which need to be considered. The first of these is the strength of association.
A strongly elevated RR is more likely to reflect a causal association than is a slightly or moderately increased risk. Consistency of findings across studies conducted with different methodologies and in different settings, is another aspect. A third characteristic is specificity, that the exposure causes a particular disease. An important condition is the sequence of events: the potentially causative factor must precede the effect (disease).
The dose-response relationship, or biological gradient, is another aspect.
1 Evans AS, 1978
2 Hill AB, 1965
3 Lilienfeld et al, 1980
Biological plausibility is an aspect which is important, but depends on the biological knowledge of the day. The association should be consistent with what is generally known about the occurrence of the disease, its natural history and pathophysiology, and should not conflict with its knowledge. The causal interpretation of an association is furthered if there is experimental evidence in support of it, for example if elimination of exposure reduces the incidence of the disease. The ninths aspect is analogy. For example, if a virus is shown to be oncogenic in animal studies, we are more prone to accept that the human papilloma virus may be the cause of cervical cancer in humans. In this essay on association and causation, Bradford Hill notes that none of his nine viewpoints can bring indisputable evidence for or against the cause-and-effect hypothesis and none can be required as a sine qua non (equivalent).