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Study Design in Molecular Epidemiology of Cancer

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Title: Study Design in Molecular Epidemiology of Cancer


1
Study Design in Molecular Epidemiology of Cancer
  • Epi243
  • Zuo-Feng Zhang, MD, PhD

2
Objectives of Molecular Epidemiology
  • To gain knowledge about the distribution and
    determinants of disease occurrence and outcome
    that may be applied to reduce the frequency and
    impact of disease in human populations.

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Epidemiological Study Design and Analysis
  • Transitional studies provide a bridge between the
    use of biomarkers in laboratory experiments and
    their use in cancer epidemiological studies.
  • The study is employed to characterization of
    biomarkers
  • The problem of the use of biomarkers
  • Serve as preliminary results rather than end
    results about cancer etiology and prevention

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Epidemiological Study Design and Analysis
  • Transitional studies
  • Measure Intra- and inter-subject variability
  • Explore the feasibility of marker use in field
    condition
  • Identify potential confounding and
    effect-modifying factors for the marker
  • Study mechanisms reflected by the biomarker

9
Transitional Studies
  • Transitional studies can be divided into three
    functional categories
  • Developmental
  • Characterization
  • Applied studies

10
Transitional Studies Developmental Studies
  • Developmental studies involved
  • determining the biological relevance
  • pharmacokinetics
  • reproducibility of measurement of the marker
  • the optimal conditions for collecting,
    processing, and storing biological specimens in
    which the marker is to be measured

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Transitional Studies Characterization
  • Assessing inter-individual variation and the
    genetic and acquired factors that influence the
    variation of biomarkers in populations

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Transitional Studies Characterization
  • Assessing frequency or level of a marker in
    populations
  • Identifying factors that are potential
    confounders or effect modifiers

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Transitional Studies Characterization
  • Establishing the components of variance in
    biomarker measurement, laboratory variability,
    intra-individual variation, and inter-individual
    variation. The ratio of intra-individual
    variation to inter-individual variation has
    important implications for study size and power

14
Transitional Studies Applied Studies
  • The applied studies assess the relationship
    between a marker and the event that it marks,
    including exposure, pre-clinical effects,
    disease, and susceptibility
  • The study is usually cross-sectional or short
    term longitudinal design and not intended to
    establish or refute a causal relationship between
    given exposure and disease.

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Transitional Studies Ethical Issues
  • The objectives of the research generally are not
    to identify health risks, but to identify
    characteristics of the biomarker or the
    distribution of the marker by population
    subtypes.
  • The meaning of the biomarker results is usually
    unknown.
  • There is a need to anticipate the impact of
    transitional studies on study subjects and plan
    to address their concerns.

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Cohort or Case-Control Studies
  • In the clinical-based cohort studies, of treated
    patients or screened populations, the inclusion
    of biological measures of exposure and
    susceptibility is both methodologically sound and
    logistically feasible

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Cohort or Case-Control Studies
  • In population-based studies, the collection of
    biological material for such markers is feasible
    but logistically more complex.
  • For early biological marker, collection of
    materials (e.g., pre-cancerous lesions) is
    logistically feasible in a hospital setting, but
    become more difficult in the population setting

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Prospective Studies Strengths
  • Exposure is measured before the outcome
  • The source population is defined
  • The participation rate is high if specimen are
    available for all subjects and follow-up is
    complete

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Prospective Studies Weaknesses
  • The usually small number of cases of each of many
    type of cancer
  • The lack of specimen if the biomarker requires
    large amounts of specimen or unusual specimens
  • Degradation of the biomarkers during long-term
    storage
  • The lack of details on other potentially
    confounding or interacting exposures

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Prospective Studies
  • The major concern of cohort studies of the short
    duration (as in case-control studies) is the
    possibility that the disease process has
    influenced the biomarker level among cases
    diagnosed within 1 to 2 years of the specimen
    being collected.

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Prospective Studies Misclassification
  • In prospective studies in longer duration, there
    may be considerable misclassification of the
    etiologically relevant exposures if the specimens
    have been collected only at baseline.
  • This misclassification occurs when individuals
    exposure level may change systematically over
    time and there may be intra-individual variation
    in biomarker level.

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Prospective Studies Intra-Individual Variation
  • The intra-individual misclassification may be
    reduced by taking multiple samples, but this will
    generally increase expenses of sample collection
    and storage and the burden on study subjects
  • Similar approaches apply to taking sample at
    several points in time in an attempt to estimate
    time-integrated exposures or exposure change.

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Prospective Studies
  • An alternative approach is to estimate the extent
    of intra-individual variation, and the
    misclassification involved in taking single
    specimens, by taking multiple specimens in a
    sample of the cohort.
  • This information can be used to correct for bias
    to the null introduced if the misclassification
    is non-differential, and therefore de-attenuate
    observed relative risks

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Prospective Studies Ethical Issues
  • Repeated contact of subjects
  • Informing the cohort members of their biomarker
    level is problematic if the biomarker is not
    considered to be sufficiently predictive of
    disease and if there is no preventive steps
    cohort members can take to reduce their risk of
    the disease

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Nested Case-Control Study
  • The biomarker can be measured in specimens
    matched on storage duration
  • The case-control set can be analyzed in the same
    laboratory batch, reducing the potential for bias
    introduced by sample degradation and laboratory
    drift

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Case-Cohort Study Design
  • Collecting the specimens at the baseline for
    entire cohort and then collecting specimens from
    cases as they occur.
  • Measuring the biomarker using newly collected
    specimen and using the baseline cohort specimen
    as control.
  • Because the specimens for cases and controls are
    taken at the different times for cases and
    controls, bias will be introduced if sample
    degradation or lab drift occurs over time

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Case-Control Study Design
  • For genetic susceptibility markers, case-control
    study design is highly appropriate
  • Clinic-based case-control studies are
    particularly suitable for studies of intermediate
    endpoints, as these end-point can be
    systematically measured.
  • Clinic-based case-control studies are excellent
    for studying etiology of precancerous lesions
    (e.g., CIN)

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Case-Control Study Design
  • Biomarkers of internal dose (e.g., carrier status
    for infectious agents, such as HBsAg) or
    effective dose (PAH DNA adducts) are appropriate
    when they are stable over a long period of time
    or when the exposures have been constant over
    exposure period. However, it is essential that
    you are not affected by the disease process,
    diagnosis, or treatment.

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The Case-Case Design
  • Applications in Tumor Markers and Genetic
    Polymorphisms Studies

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Case-Case Study Design
  • To identify etiological heterogeneity
  • To evaluate gene-environment interaction

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Case-Case Study Design
  • Case-only, Case-series, etc.
  • Studies with cases without using controls
  • Can be employed to evaluate the etiological
    heterogeneity when studying tumor markers and
    exposure
  • May be used to assess the statistical
    gene-environment or gene-gene interactions

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Interaction Assessment using Case-Control Study
  • Genotype abnormal OR1
  • Genotype normal OR2
  • Interaction measure OR1/OR2
  • here OR2OR01
  • OR1OR11/OR10
  • OR Interaction OR11/(OR10xOR01)

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Comparison of Case-Control and Case-Case Study
designs
Parameter Case-control Case-Case
Beta(01) OR01 Not measured
Beta(10) OR10 Not measured
Beta interaction ORint OR11/OR01xOR10 Measured
Beta (11) OR11OR01 x OR10 x ORint Not measured
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Assumptions for Case-Case Study Design
  • Exposure and genotype occur independently in the
    population
  • The Risk of disease is small (or the disease is
    rare) at all level of the study variables

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Smoking and TGF-alpha Polymorphism
Smoking TGF-B Case Control OR adj.
Never Normal 36 A00 167 B00 1.0 OR00
Never Positive 7 A01 34 B01 1.0 OR01
Yes Normal 13 A10 69 B10 0.9 OR10
Yes Positive 13 A11 11 B11 5.5 OR11
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OR int OR11/(OR01 x OR10) 5.5/(1.0 x
0.9)6.1 OR CA(A11 x A00)/(A10 x A01) (13 x
36)/(13 x 7)5.1
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OR intOR CA/OR COOR 11/(OR01xOR10) OR11A11
B00/A00 B11 OR CA OR 11/(OR01xOR10) x OR
CO Assumption OR CO1, OR int OR CA
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Sample Size
Main effect Interaction
Case-control (RR) 2.0 (RR) 2.0
Sample size 150 cases 150 controls 600 cases 600 controls
Case-Case 300 cases
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Strengths of Case-Case Study Design
  • Case-Case study design offers greater precision
    for estimating gene-environment interaction than
    case-control study design
  • The power for detecting gene environment
    interactions in case-case study is comparable to
    the power for assessing a main effect in a
    classic case-control study. Which leads to
    reduced sample size for interaction assessment.

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Strengths of Case-Case Study Design
  • Only cases are needed, thus avoiding the
    difficulties and often unsatisfying selection of
    appropriate controls (avoiding selection bias for
    controls)

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Limitations of Case-Case Study Design
  • The main effects of susceptible genotype (G) and
    environment effect (E) cannot be estimated
  • The case-case study will miss gene-environment
    models with departures from additivity.

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Intervention Studies
  • In studies of smoking cessation intervention, we
    can measure either serum cotinine or protein or
    DNA adducts (exposure) or p53 mutation, dysplasia
    and cell proliferation (intermediate markers for
    disease)
  • Measure compliance with the intervention such as
    assaying serum b-carotene in a randomized trial
    of b-carotene.

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Intervention Studies
  • Susceptibility markers (GSTM1) can also be used
    to determine whether the randomization is
    successful (comparable intervention and control
    arms)

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Family Studies
  • Does familial aggregation exist for a specific
    disease or characteristic?
  • Is the aggregation due to genetic factors or
    environmental factors, or both?
  • If a genetic component exists, how many genes are
    involved and what is their mode of inheritance?
  • What is the physical location of these genes and
    what is their function?

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Issues in Study Design and Analysis
  • Relating a particular disease (or marker of early
    effect) to a particular exposure while
    minimizing bias controlling for confounding
    assessing and minimizing random error and
    assessing interactions

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Sample Size and Power Consideration
  • EPI243 Molecular Epidemiology of Cancer

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Sample Size and Power
  • False positive (alpha-level, or Type I error).
    The alpha-level used and accepted traditionally
    are 0.01 or 0.05. The smaller the level of alpha,
    the larger the sample size.

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Sample Size and Power
  • False negative (beta-level, or Type II error).
    (1-beta) is called the power of the study.
    Investigator like to have a power of around 0.80
    or 0.95 when planning a study, which means that
    there have a 80 or 95 chance of finding a
    statistically significant difference between
    study and control groups.

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Sample Size and Power
  • The difference between study and control groups
    (delta). Two factors need to be considered here
    one is what difference is clinically important,
    and the another is what is the difference
    reported by previous studies.

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Sample Size and Power
  • Variability. The more the variability of the
    data, the bigger the sample size.

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Power or Sample Size Estimate for Case-Control
Studies
  • Alpha-level (false positive) 0.05
  • Beta-level (false negative level 1-betapower)
    0.20
  • Delta-level Proportion of exposure in controls
    and exposure in cases or expected odds ratio

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Power Estimate
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Sample Size Estimate
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Estimate Minimum Detectable Odds Ratios
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Gene-Environment (Gene-Gene) Interaction
  • EPI242 Molecular Epidemiology
  • Zuo-Feng Zhang, MD. PhD

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Definition for Interaction
  • Interaction (effect modification) occurs when the
    estimate of effect of exposure depends on the
    level of other factor in the study base.
  • Interaction is distinct from confounding (or
    selection or information bias), but rather a real
    difference in the effect of exposure in various
    subgroup that may be of considerable interest.

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Interaction Assessment
Factor A
Absent Present
Factor A Absent RR00 RR01
Present RR10 RR11
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Interaction Assessment
  • RR00, relative risk when both factors absent
  • RR01, relative risk when factor A present only
  • RR10, relative risk when factor B present only
  • RR11, relative risk when both factors A B
    present

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Interaction Assessment
  • Combined RR RR11
  • RR11 gt RR01 x RR10 indicating more than
    multiplicative interaction
  • or RR11/RR10 gtor lt RR01/RR00
  • or RR11/RR01xRR10 gt or lt 1
  • Interaction RR RR11 / (RR01 x RR10)

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Odds Ratios for two factors,Interaction?
Factor B
absent present
Factor A absent 1.0 2.5
present 4.0 10.0
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No more than multiplicative interaction
  • ORs for factor B 2.5 when factor A present 2.5
    (10.0/4.0) when factor A absent
  • ORs for factor A 4.0 when B absent and 4.0
    (10.0/2.5) when factor B present

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Odds Ratios for two factors,Interaction?
Factor B
absent present
Factor A absent 1.0 2.5
present 4.0 20.0
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More than Multiplicative Interaction, Positive
Quantitative Interaction
  • ORs for factor B 2.5 when factor A absent 5.0
    (20.0/4.0) when factor A present
  • ORs for factor A 4.0 when B absent and 8.0
    (20.0/2.5) when factor B present

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Odds Ratios for two factors,Interaction?
Factor B
absent present
Factor A absent 1.0 2.5
present 4.0 5.0
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More than Multiplicative Interaction, Negative
Quantitative Interaction
  • Both factors increase the risk regardless of the
    value of the other factor, but the combined
    effect is less than the product of the two,
    although greater than that of either factor
    alone, giving a negative quantitative
    interaction.

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Odds Ratios for two factors,Interaction?
Factor B
absent present
Factor A absent 1.0 2.5
present 4.0 4.0
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More than Multiplicative Interaction, Negative
Quantitative Interaction
  • Both factors increase the risk
  • When A is present, there is no additional effect
    of factor B
  • Adding factor A to factor B, only increases the
    risk to the degree found for factor A alone
    (4.0), leading to negative quantitative
    interaction.

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Sample Size Consideration for Interaction
Assessment
  • Evaluation of interaction requires a substantial
    increase in study size. For example, in a
    case-control study involves comparing the sizes
    of the odds ratios (relating exposure and
    disease) in different strata of the effect
    modifier, rather than merely testing whether the
    overall odds ratio is different from the null
    value of 1.0.

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Sample Size Consideration
  • The power to test interaction depends on the
    number of cases and controls in each strata (of
    the effect modifier) rather than overall numbers
    of cases and controls.
  • When considering possible interactions, the size
    of the study needs to be at least four time
    larger than when interaction is not considered
    (Smith and Day)

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