Hypothesis generating and testing

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Hypothesis generating and testing

• Epi 242 Cancer Epidemiology
• Fall, 2009

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Descriptive Epidemiology

• It is concerned with the distribution of disease,
  including consideration of
• what populations or subgroups do or do not
  develop a disease (person),
• in what geographic locations it is most or least
  common (place).
• how the frequency of occurrence varies over time
  (time)

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Descriptive Epidemiology

• It is associated with frequency and distribution
  of the disease. It describes the general
  characteristics of the distribution of a disease
  with regard to person (age, sex, race, marital
  status, occupation, etc.) place (variation among
  countries within countries urban/rural areas)
  and time (seasonal pattern in disease or time
  changes of the disease frequency). Information on
  each of these characteristics can provide clues
  leading to the formulation of an epidemiologic
  hypothesis that is consistent with existing
  knowledge of disease occurrence.

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Descriptive Epidemiology

• There are three types of study design for
  descriptive epidemiology (1) case report and
  case-series study design are at the individual
  level (2) correlation study or ecologic study
  designs are at the population level and (3)
  cross-sectional study design is at the individual
  level.

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Descriptive Epidemiology Population Distribution

• Distribution of cancer in relation to person.
• Who is getting the disease?
• Demographic factors age, sex, race, marital
  status, occupation.
• Age, sex, and race are three most important
  factors in cancer descriptive epidemiology.
• Age specific cancer rate (Figure).

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Graph 1 indicates that an exogenous agent, acting
continuously throughout life, is believed as the
major etiologic factors as in lung and esophageal
cancers.
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Graph 2 suggests that the etiologic factors are
strongest in early life. The decreased rate in
very old age group could be explained
byDiminished exposure to an exogenous agent or
a birth cohort effectElimination of a
susceptible population subgroup (competing
risk)Changes in host occurring in meddle age, as
age at menopauseSerious under-reporting in old
age.
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• Graph 3 (bimodal curve) as seen in breast
  cancer suggesting different etiologic factors act
  in early and late life.

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• Graph 4 Suggests a strong etiologic factor at
  the early age such as liver cancer

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• Graph 5, The curve peak in childhood and slow
  increase in later life as seen in leukemia or
  sarcomas, also indicates two different carcinogens

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• Graph 6 indicate the small number of cases and
  may not be reliable.

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Figure 3 Age has no effect on susceptibility to
some carcinogens. Left panel, cumulative
mesothelioma risk in US insulation workers. Right
panel, cumulative skin tumour risk in mice
treated weekly with benzo(a)pyrene. Mesothelioma
rates in humans65 and skin tumour rates in mice64
depend on time since first carcinogenic exposure
but not on age, suggesting an initiating effect
of these carcinogens. Lung cancer incidence in
smokers depends on duration of smoking but not on
age, and stops increasing when smoking stops67,
indicating both early- and late-stage effects.
Radiation-induced cancer incidence increases with
age at exposure above age 20, suggesting
predominantly late-stage effects3, although the
large effect of childhood irradiation also
indicates an early-stage effect.
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Geographic Distributions

• Distribution of cancer in relation to place.
  Where are the rates of disease highest and
  lowest?
• Variations among countries
• Variations within countries, such as between
  urban and rural areas

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Distribution of cancer according to time

• Is the cancer rate at present different from the
  cancer rate in the past?
• Seasonal patterns of the disease
• Time trends of the disease

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Tobacco Use in the US, 1900-1999
Per capita cigarette consumption
Male lung cancer death rate
Female lung cancer death rate
Age-adjusted to 2000 US standard
population. Source Death rates US Mortality
Public Use Tapes, 1960-1999, US Mortality
Volumes, 1930-1959, National Center for Health
Statistics, Centers for Disease Control and
Prevention, 2001. Cigarette consumption Us
Department of Agriculture, 1900-1999.
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Trends in Ethanol Consumption in the US, 1960-97
Source NIAAA, NIH
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Trends in oral cancer incidence rates in 9 SEER
areas in the US by gender and race from 1973-1975
through 1996-2000
Age standardized to 2000 US population
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Trends in Overweight Prevalence (), Adults 18
and Older, US, 1992-2001
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Trends in esophageal cancer incidence rates in 9
SEER areas in the US by gender, race, and cell
type from 1973-1975 through 1996-2000
Age standardized to 2000 US population
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Change of the cancer rates may be caused by many
factors

• Changes in diagnostic techniques
• Changes in accuracy of tumor registry
• Changes in age distribution may cause the
  increase in crude rates
• Changes in survivals
• Improved treatment
• Early diagnosis or screening
• Changes in actual incidence of disease due to
  alterations in environmental or life-style factors

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The Sequence of Investigation for Etiology of
Disease

• Formulating hypotheses
• Testing hypotheses
• Intervention

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Formulate Hypotheses

• The clinician makes an observation regarding
  cause, based on his/her experience (case
  report/case series study). The epidemiologist
  describes the distribution of the frequency of
  the disease with regard to person, place, and
  time (ecological studies, cross-sectional
  studies). In addition, the laboratory data will
  also supply certain information regarding to
  potential causes for the disease. These data from
  different sources can be employed to formulate
  the hypotheses.

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Testing Hypotheses

• These hypotheses may be tested in sequence by
  retrospective (case-control) studies, and if the
  results are positive, by the prospective (cohort)
  studies. Sometimes, there are only case-controls
  studies since prospective studies take a long
  time to accomplish.

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Intervention

• If risk factors are identified by both
  retrospective/prospective studies, an
  intervention trial may be designed to ascertain
  whether or not modification of such factors is
  followed by a reduction in amount of disease.

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Hypothesis Generating

• A new hypothesis can affect the direction of
  future research and the success or failure of the
  research depends on the soundness of the
  hypothesis.
• By observing patterns and distribution of cancer
  incidence, three methods of hypothesis
  formulation about disease etiology.

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Method of Difference

• If the frequency is markedly different in two
  sets of circumstances, the disease may be caused
  by some particular factor that differs between
  them.
• If the cancer rate is very rare in one country,
  but very common in another country, it may
  suggest potential life-style or environmental
  exposures.

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Method of Agreement

• The observation that a single factor is common to
  a number of circumstances in which a disease
  occurs with a high frequency.
• Cervical cancer occurs higher in women with
  multiple sexual partners, in women whose husbands
  had multiple sexual partners, in women whose
  husbands had penial cancer. All those
  circumstances indicate that a sexually
  transmitted agent/agents may play an important
  role in the etiology of cervical cancer.

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Method of Concomitant Variation

• The frequency of a factor varies in proportion to
  the frequency of disease. Correlation studies are
  particularly useful sources of data for this type
  of hypothesis formulation.

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Considerations in the Formation of Hypotheses

• Biological basis and support of the hypothesis
• New hypotheses are commonly formed by relating
  observations from several different fields (e.g.,
  clinical, pathological, and laboratory
  observations)
• The stronger a statistical association, the more
  likely it is to suggest a causal hypothesis (when
  you generate hypothesis from existing data).

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Considerations in the Formation of Hypotheses

• Observation of changes in frequency of a disease
  over time, especially changes that have occurred
  over the relatively short period of time (lung
  cancer, adenocarcinoma of esophageal cancer,
  etc.) 
• Clustering unusual cases of cancer may indicate
  the potential environmental exposures

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Starting A Hypothesis

• Study subjects the characteristics of the
  persons to whom the hypothesis applies.
• The risk factor or potential cause environmental
  or genetic factors
• The disease the expected effect
• The exposure-response relationship
• The time-response relationship
• e.g., By reducing dietary fat from 40 to 20 in
  white males with elevated PSA, the incidence of
  prostate cancer will reduce 30 within five years
  in this population.

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Hypothesis Testing

• Study Design for Hypothesis Testing. There are
  several types of epidemiologic studies
  Prospective or retrospective studies are
  classified according to time frame of the study
  observational or experimental epidemiological
  studies are depended on whether or not the
  investigator has control of some factors
  (intervention factors, treatment) that may be
  associated with a different outcome and
  descriptive or analytic studies are based on
  purposes of the study designs (formulating or
  testing hypotheses).

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Hypothesis Testing

• Analytic Epidemiology deals primarily with the
  determinants of the disease. In analytic study
  design, the investigator assembles groups of
  individuals to determine whether or not the risk
  of disease is different for individuals exposed
  then it is for individuals not exposed to a
  factor of interest.

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Hypothesis Testing

• There are three types of study design (1)
  case-control (case-reference) studies
  (observational study) (2) cohort
  (retrospective/prospective) studies
  (observational study) (3) intervention studies.
  We will focus our discussion on two major study
  designs Case-control studies and prospective
  studies.

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The Framework for the Interpretation of An
Epidemiological Study

• Is there a valid statistical association?
• Is the association likely to be due to chance?
• Is the association likely to be due to bias?
• Is the association likely to be due to
  confounding?

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The Framework for the Interpretation of An
Epidemiological Study

• Can this valid statistical association be judged
  as cause and effect?
• Is there a strong association?
• Is there biologic credibility to the hypothesis?
• Is there consistency with other studies?
• Is there evidence of a dose-response
  relationship?
• Is the time sequence compatible?