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Clin Transl Oncol (2007) 9:290-297
DOI 10.1007/s12094-007-0056-x
Red series*
Cancer epidemiology: study designs and data analysis
N. Malats and G. Castaño-Vinyals
Centre de Recerca en Epidemiologia Ambiental (CREAL). Institut Municipal d’Investigació Mèdica (IMIM). Barcelona, Spain
Abstract Among the scientific interests of cancer epidemiology is the identification of both environmental
and genetic factors associated with cancer development.
Observational designs requiring sophisticated methodology are applied to control for potential confounding factors. The enormous biotechnological potential developed in the last two decades has allowed the integration
of a plethora of new biomarkers in epidemiological
studies to better define the exposure and “neoclassic”
outcomes, as well as incorporating genetic susceptibility
factors in both classical and new epidemiological designs. The integration of scopes, objectives, data and
tools coming from different disciplines also benefits
epidemiology, thus evolving into “systems epidemiology”. In this manuscript, we review the basic concepts of
study designs and data analysis and introduce readers to
the more innovative aspects that are now being applied
in epidemiological studies.
Key words Case-control study • Exposure biomarker •
Disease biomarker • Genetic susceptibility • Data analysis
Malats N, Castaño-Vinyals G (2007) Cancer epidemiology:
study designs and data analysis. Clin Transl Oncol 9:290-297
*Supported by an unrestricted educational grant from
Roche Farma S.A.
N. Malats (쾷)
Centre de Recerca en Epidemiologia Ambiental (CREAL)
Institut Municipal d’Investigació Mèdica (IMIM)
Carrer del Dr. Aiguader, 88
E 08003 Barcelona, Spain
E-mail: [email protected]
Introduction
Epidemiology aims at identifying the determinants that
affect health in the population, at assessing their distribution according to specific subgroups of individuals
and at controlling the diseases [1]. Cancer is a major
area of epidemiological interest because of its importance as a public health problem [2]. The scientific focus of cancer epidemiology ranges from aetiological objectives, aimed at identifying both environmental and
genetic factors associated with cancer development, to
more clinical aims that involve both diagnostic and
prognostic aspects. In this paper, we comment only on
the aetiological arena of cancer epidemiology.
The research on cancer epidemiology has allowed
estimation of the percentage of cancer cases attributed
to several environmental exposures and lifestyle factors
– attributable risk. For example, tobacco use accounts
for 30% of all cancer cases, unhealthy diet for 10–25%,
obesity for 15%, physical inactivity for 5%, alcohol
consumption for 4% and viruses for 3% [3–5].
Epidemiology deals with people, hence ethical issues often hamper their inclusion in experimental studies. Instead, “observational” designs are commonly applied, the most classical being case-control and cohort
studies. This limitation has forced epidemiology to develop a sophisticated and complex methodology to control for potential confounding factors. This methodology
mainly regards the type of design, selection of subjects
and sample size, collection of information and statistical
analyses.
While what is known as “classical” epidemiology restricts its interest to environmental exposures, “molecular” and “genetic” epidemiology apply a wide variety of
biomarkers to better define both the exposure and “neoclassic” outcomes, as well as the inherited genetic patterns that predispose or protect against the disease or the
variation in its natural history.
The enormous biotechnological potential developed
in the last two decades has allowed the integration of a
plethora of new biomarkers in epidemiological studies,
such as gene expression, genetic polymorphisms and
epigenetic patterns. This fact is provoking, at the same
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time, the development of methodological innovations
regarding all aspects of epidemiology: new designs and
information, including the development of strategies to
reduce the very large sets of genetic information, new
statistical analytical tools and methods to adjust for genetic complexity. This is the era of integration of scopes,
objectives, data and tools coming from different disciplines such as molecular and cellular biology, genetics,
toxicology, statistics, bioinformatics and clinical specialities. Epidemiology also benefits from this integrative approach, thus evolving into “systems epidemiology”.
In this manuscript, we review the basic concepts of
study designs and data analysis and introduce readers to
the more innovative aspects that are now being applied
in epidemiological studies.
291
1) 2 x 2 table
Exposed
Non-exposed
ORcrude =
Cases
Controls
A1
C1
A0
C0
A1 / A1 + C 1
1 − ( A1 / A1 + C 1) A1xC 0
=
A0 / A0 + C 0
C 1xA0
1 − ( A0 / A0 + C 0)
If confounder is present:
If confounder is absent:
Cases
Controls
Exposed
A1
C1
Non-exposed
A0
C0
Cases
Controls
Exposed
A1
C1
Non-exposed
A0
C0
OR2
OR1
Classical study design and analytical strategies
Research studies can be classified in two main groups:
experimental and observational. The first is mostly a
laboratory-based approach that allows the researcher to
manipulate the conditions of the exposed subjects. On
the other hand, in observational studies the researcher
“just” observes the subjects’ behaviour and exposures
and assesses their association with disease occurrence
[6].
There are different study designs on individuals as
observation units: cohort, case-control, cross-sectional
and case-crossover studies [7]. Probably, the most common design is the case-control study. It consists of identifying diseased – cases – and healthy subjects – controls, collecting past history of exposures, and then
assessing whether there are differences in the distribution of exposures between cases and controls through
association tests that are interpreted as the exposure effect. In addition to an appropriate sample size, the success of this design relies on very careful selection of
controls. The natural history and the relatively low frequency of cancer in the population (12% of all mortality
causes) make it an ideal topic to be studied by case-control design.
The measure of association most commonly used in
case-control studies is the odds ratio (OR), defined as
the ratio of the odds of developing the disease. This is
an approximation of the relative risk used in cohort
studies (see below). As in case-control studies the information on all subjects that are not exposed is lacking,
the measure of risk we obtain should be derived from
the available information (see Fig. 1). Without getting
into further details of the development of the equation,
the OR is the cross-product ratio, or (A×D)/(B×C). The
statistical analysis method applied to test whether the
observed distribution statistically departs from the expected one is the χ2 test of association.
2) 2 x 3 table
Cases –
Cases –
group 1
group 2
Exposed
A1
B1
C1
Non-exposed
A0
B0
C0
OR group1 =
A1xC 0
C 1xA0
Controls
OR group2 =
B1xC 0
C 1xB 0
3) 2 x 4 table for gene-environmental assessing.
The 2 x 2 table (model 1) for risk assessment in the context of case-control studies can
also be displayed as:
Exp/gene
Cases
Controls
0
A0
C0
Odds ratio
1
1
A1
C1
A1C0 / A0C1
A simple gene-environmental interaction model in the context of case-control studies is
then displayed in a 2 x 4 table
Exposur
e
Susceptibility
genotype
0
Case-control study
Cases
Controls
0
A00
C00
Odds ratio
1
0
1
A01
C01
Rg=A01C00/A00C01
1
0
A10
C10
Re=A10C00/A00C10
1
1
A11
C11
Rge=A11C00/A00C11
4) 2 x 2 table for case-only design
Exposed
Non-exposed
Cases —
Cases —
group 1
group 2
A1
B1
A0
B0
ORcrude =
A1xB 0
B1xA0
Fig. 1 Cross-tabulation of exposure and disease
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When the OR only takes into account one exposure,
it is called the crude OR. Confounding by a third factor
–whether an exposure or not– can bias this association.
A confounder is defined as a factor that is causally associated with the outcome; it is also associated with the
exposure (either causally or non-causally) and it is not
an intermediate in the causal pathway between the exposure and the outcome. In the case of suspicion of the
presence of a confounder, this should be checked by
stratifying the association according to the potential
confounder strata, obtaining one OR for each category
in the confounder variable. If the presence of a confounder is verified, an adjusted model should be used, with
the Maentel-Haenszel method, yielding an ORadjusted.
For more complex modelling, such as adjusting for
several factors, statistical packages need to be used. For
a binary outcome, such as diseased and non-diseased
subjects, the logistic regression model is the one used to
estimate the association between case-control status and
an exposure. This method allows adjustment for different variables.
Unlike case-control studies, cohort studies recruit
healthy subjects, regardless of exposures, till they develop
the disease of interest –i.e., cancer– and assess the association between exposure and disease by providing estimates of disease incidence and survival analysis methods. The estimate of risk computed in cohort studies is
named relative risk (RR). The RR is the ratio of the risk
of developing the disease among the exposed individuals over the risk among the unexposed. This is actually a
ratio of incidence rates among the exposed and non-exposed individuals. An important limitation of this design
is the common need for very extensive follow-up periods and the relatively large number of drop-outs for the
study to get enough cases and sufficient statistical power. For many cancers, this requires up to 20 years.
Molecular epidemiology: from exposures to the disease
“Analysis of biomarkers is increasingly being incorporated into cross-sectional, retrospective, prospective, or
nested case-control studies to gain improved resolution
of the risk factors and mechanisms responsible for cancer.” [8]
Carcinogenesis is a multistage process evolving
from environmental carcinogen exposure to the development of preneoplastic and neoplastic phenotypes and to
cancer progression. It is now possible to obtain biochemical and molecular information – biomarkers – of
the intermediate states in this process and integrate it in
large epidemiological studies [9, 10]. The use of biomarkers in epidemiology constitutes so-called molecular epidemiology [11].
In a broader sense, a biomarker is a parameter that
we can measure in humans, in general using non-inva-
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sive techniques, and that is relevant for our object of
study [12]. An already classical scheme has been widely
used to describe the molecular epidemiology path [8,
13–15].
The aims of using biomarkers in epidemiologic studies are to increase the sensitivity and specificity in detecting exposure to carcinogens, to evaluate more precisely the interplay between genetic and environmental
determinants of cancer, to detect earlier the pro-carcinogenic effects of exposures, to characterise the disease
subtypes and to evaluate primary prevention measures
[16]. An additional objective is to consider inherited genetic variants (polymorphisms) as effect modifier factors – factors that modify the effect/association of environmental factors with a disease by either increasing or
decreasing the risk. This phenomenon is called gene–environment interaction. The advances in detailing the
complex aetiological picture of cancer demands the application of new designs – such as the case-only, casecase-control, case-parental control and affected sib-pair
– among others, for the comparison of the frequency of
factors among unrelated affected and unaffected individuals. In addition to these new designs, molecular epidemiology studies often demand large sample size, and
innovative methods on biobanking, selection of subjects
and statistical analyses. Table1 displays free online interactive links of databases and statistical packages
Biomarkers can be broadly classified into biomarkers of exposure, effect and susceptibility. The first category is subdivided in internal dose biomarkers, which
mainly include the chemical compound – or its metabolites – detected in biological media, and biological effective dose biomarkers, mainly adducts of DNA or proteins [17–19]. When talking about biomarkers of effect,
early biological effect biomarkers – such as chromosomal aberrations, sister chromatid exchanges, micronuclei and specific gene mutations –as well as subclinical
and clinical disease biomarkers and prognostic biomarkers– are considered. The former group in the category of
effect biomarkers can be used as exposure makers as
well (see Fig. 2, panel B). Markers of susceptibility include a wide category of conditions, both genetic and
non-genetic, that can participate at all the stages of carcinogenesis [13]. There is no perfect classification: recent reviews suggest no longer using this classification
of biomarkers, as one specific group of them, i.e., DNA
adducts, can integrate exposure, effect and susceptibility
[20].
Biomarkers
Once a substance enters the body, it undergoes metabolic transformation. An internal dose biomarker is the
measure of either the parent substance or one of its
metabolites in one of the body fluids, such as urine,
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Table 1 Free online interactive links
Databases
Databases
Entrez Gene
OMIM
National Cancer Institute
IARC Handbooks on Cancer Prevention
IARC Monographs on the Evaluation of Carcinogenic
Risks to Humans
National Center for Health Statistics,
National Health and Nutrition Survey
NCI Early-Detection Research Network
US Surgeon General Reports on Smoking and Health
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM
http://www.cancer.gov
http://www.iarc.fr/IARCPress/general/prev.pdf
http://monographs.iarc.fr/
http://www.cdc.gov/nchs/nhanes.htm
http://www3.cancer.gov/prevention/cbrg/edrn/
http://profiles.nlm.nih.gov/NN/ListByDate.html
Packages to estimate statistical power and sample size (from Cordell and Clayton [35])
QUANTO
Genetic power calculator
Stata power and sample size programs
TDTPOWER71
TDTASP
TDT-PC
http://hydra.usc.edu/gxe
http://statgen.iop.kcl.ac.uk/gpc/index.html
http://cruk.leeds.ac.uk/katie
http://www.uni-bonn.de/~umt70e/soft.htm
http://odin.mdacc.tmc.edu/anonftp/
http://www.biostat.jhsph.edu/~wmchen/pc.html
Standard statistical packages
Stata
SAS
S-Plus
R
http://www.stata.com/
http://www.sas.com/
http://www.insightful.com/products/splus/
http://www.r-project.org/
blood or saliva [13]. Several biomarkers of exposure to
PAHs were analysed in children exposed to air pollution, such as measurement of 1-hydroxyperene [21]; this
marker was also used is several environmental and occupational studies of exposure to carcinogens [22, 23].
Internal dose biomarkers usually replace environmental
exposure in classical study designs, both case-control
and cohort studies.
In a subsequent stage in the carcinogenesis process,
biomarkers of biologically effective dose measure the interaction between the carcinogen and cellular substructure molecules such as DNA or proteins [13], DNA
adducts being the most representative type. An adduct
can be defined as the covalent bond between a biological molecule – such as DNA or proteins – and a toxic
substance, either the parent compound or one of its
metabolites. The half-life of these adducts depends on
both the half-life of the biological molecule and the stability of the chemical. The study of adducts can yield
clues about the mechanism of the disease; Peluso et al.
found a positive association between DNA-PAH adducts
and lung cancer [24]. In a meta-analysis of DNA
adducts and risk of cancer [17], an association between
the biomarkers studied and the risk of lung and bladder
cancer was observed, restricted to current smokers.
Again, these types of markers usually replace – or refine – environmental exposures in classical study designs, both case-control and cohort studies.
Early biological effect biomarkers are those alterations that have a biological effect, sometimes irre-
versible, due to the interaction with a toxic substance.
Their detection depends on the DNA repair capacity and
the cellular turnover. They may be specific and non-specific. The most classical assays that refer to the non-specific markers are chromosomal aberrations, sister chromatid exchanges and micronuclei [25, 26]. Among the
specific marker group, there are those that imply
changes in a key oncogene or tumour suppressor gene,
such as alterations in the Tp53 or ras genes, cases being
subclassified according to these alterations.
As mentioned before, these can be considered as
biomarkers of exposure, effect and even susceptibility
[20]. If they are “used” as effect markers, they are included in case-control studies by splitting the case
group according to the molecular characteristics of cases. So, we would have 2 or more groups of cases and
one control group (Fig. 1.2). Analytically, each one of
the case groups is compared with the control group by
applying the so-called multinomial or polytomous logistic regression. Usually, only two groups of cases are
considered, those with and those without the molecular
characteristic of interest. Statistically speaking, it would
be a case-case-control analysis. There is evidence from
the literature on studies that observe differences between the case subgroups. Porta et al. studied the association between serum concentrations of several
organochlorine compounds, such as PCBs, DDT and
DDE, and K-ras mutations in pancreatic cancer. DDT
and DDE concentrations were significantly higher
among K-ras mutated cases than among K-ras wild-type
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Fig. 2 Molecular cancer epidemiology models
cases and controls [27]. Other examples consider the Nras mutations and occupational exposures in patients
with acute myeloid leukaemia [28], to K-ras mutations
and asbestos exposure in lung adenocarcinoma [29], and
to K-ras p21 protein and vinyl chloride exposure in angiosarcomas of the liver [30]. A more recent example
studied different dietary characteristics –as exposure
factors– in relation to colorectal adenomas [31]. Cases
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were classified according to the K-ras mutation, and
each group (mutated or wild-type) was compared with
the control group, finding differences in the risk for the
disease depending on the K-ras status.
Susceptibility markers provide information on those
physiological statuses, both genetic and non-genetic,
that predispose the individuals to develop the disease of
interest. Among the non-genetic markers, we can find
the nutritional, immunological and disease statuses [13],
as well as global methylation levels [32].
Regarding inherited factors, genetic variants can be
classified according to their penetrance in two groups:
high and low penetrance genetic factors. The latter
group comprises most of the inherited variations in a
gene sequence that translate into a functional variation
(i.e., gene expression levels, protein function, among
others) that do not cause the disease by itself but predispose to its development. These markers are classified as
genetic susceptibility factors. We broadly name these
variants as polymorphisms. Many of them are single nucleotide polymorphisms (SNP), meaning the molecular
change involves only one nucleotide. The number of
SNPs estimated in the genome exceeds 10,000,000 and
they can be transmitted together within the population
(i.e., in linkage disequilibrium) following haplotype
blocks.
The choice of strategies to identify genes that increase/decrease individual susceptibility to develop cancer depends on the number of genes involved (for a specific site), their frequency, penetrance and interactions.
When low-frequency alleles, dominant, and of moderate-high penetrance, are suspected to be involved in the
development of a specific phenotype, linkage analysis
in extended “pedigrees” of individuals in whom cancer
was diagnosed at an early age or with strong familial
aggregation (i.e., breast and colon cancer) is proposed
to be applied. On the contrary, when it is suspected that
multiple and frequent loci are involved, following recessive inheritance patterns, or more complex patterns resulting from interactions with environmental factors, association/observational studies is the best option [33].
As described, this type of study can provide sufficient power to distinguish slight variations in disease
risks, being more sensitive than linkage methods when
the genes of interest contribute to disease susceptibility
but are neither necessary nor sufficient to cause disease.
Association studies imply the comparison of the frequency of candidate alleles in candidate genes, or
TagSNPs along the genome (whole genome scan),
among unrelated affected and unaffected individuals.
The alleles analysed may be thought to contribute to the
disease or be in linkage disequilibrium with any such
causative variation. Another advantage of these approaches is that they use the same methodology as epidemiological studies (cohort and case-control design),
although new non-traditional designs have also been
proposed.
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Case-control is, again, the most commonly applied
design. It overcomes the assumption of temporality –as
the genetic cause always precedes the effect– and it allows the assessment of a large number of genetic and
environmental factors, as well as their interaction.
Furthermore, this design provides the opportunity to
conduct unique analyses such as the case-control design
with extreme phenotypes. To assess gene×environment
interaction, the 2×2 table expands (2×4 table) to include
an additional column that refers to the distribution of
subjects according to genetic factor (see Fig 1.3). The
first row of the table is taken as the reference category,
the OR assessed in the next two refer to the effect of the
environmental or genetic factors alone, and the last row
to the joint effect (gene×environment interaction).
Gene×environment interactions can also be assessed
through a case-only design conducted only with cases
(see Fig 1.4). Instead of controls, this design distributes
exposures between cases with and without the genetic
characteristic [34]. This design does not allow investigators to evaluate the independent effect of the exposure
alone or the genotype alone. The interaction assessed
departs from the multiplicative effect and it assumes independence between exposure and allele. The associations observed may be due to linkage disequilibrium between the genetic marker and the true susceptibility
allele at a neighbouring locus.
In addition to misclassification of genotyping and
phenotypes, and confounding by population structure,
lack of statistical power and false-positive results due to
chance because of multiple testing are important caveats
of this type of designs. This is mainly so when gene×environmental interactions are being assessed.
There are other, yet less popular, designs that are
placed in between familial and population studies and
that are suitable to explore genetic associations under
certain conditions. Among them are the affected sib-pair
and the case-parent triads. The latter consists of assessing the significance of the parental and the case allele’s
distribution through the transmission/disequilibrium
test, conditional logistic regression or log-linear models
[35]. This approach also allows analysis of gene×environment interactions by stratifying the allele distribution
tables according to the presence or the absence of the
environmental exposure. This type of study is less powerful than case-control studies and requires the genotyping of the parents; hence, for most patients with the
common types of cancers, it cannot be applied. On the
other hand, it is robust to genetic stratification and can
estimate maternal and imprinting effects [35].
The integration of epidemiology and bioinformatics
in cancer research
If you do not look for the unexpected you will never
find it because it is painful and hard to find.
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Heraclito of Efeso, Centuries VI-V B.C.
Among factors hampering the progress in cancer
epidemiological research are the different molecular and
pathological phenotypes of the disease, the complex nature of the yet unknown environmental factors, the complexity of the genetic networks acting at different steps
in carcinogenesis, and the dynamic interaction of causes
acting during the natural history of the disease. We have
discussed how the integration of information from toxicology and molecular biology permits the first two
points to be dealt with. As for the last two, the interaction of epidemiology with bioinformatics should allow
epidemiology to move ahead. The potential of information technology in managing large amounts of information and searching for dynamic interactions between
factors –through what is currently known as systems biology methods– should also be considered in epidemiology, adding a new dimension, that of systems epidemiology.
As with systems biology, epidemiology should consider the integration of large amounts of data from high
throughput sources both to create new hypotheses and to
build disease models. Computational models allowing
the exploration of dynamic data may be a useful tool for
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