Determining the
Etiology of Disease

Descriptive Studies:
Data Collection and
Analysis
Analyze results and retest
Model Building and hypothesis formulation
Analytic Studies for
Hypothesis testing

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Must have statistical significance
Association may be either positive or negative (if positive, the association is higher than expected; if negative the association is lower than expected)
Must try to rule out “noise” (assuring the comparison of “apples to apples” by controlling confounding factors)

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A false or fictitious association can result from chance occurrence or bias in the study methods
Type 1error occurs from random fluctuation
Through retesting, we can determine
“spurious relationships.
Non-causal associations take place when a factor and disease are associated indirectly

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Strength of association
Dose-Response Relationship
Consistency of the Association
Temporally Correct Association
Specificity of the Association
Coherence with Existing Information

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Primary data – information collected directly by the researcher
Secondary data – data that has already been collected and stored for analysis

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Administrative surveys, medical records, vital records and statistical data
Telephone surveys
Self-administered surveys
Personal interviews

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Measurement is an attempt to assign numbers to observations according to a set of rules
Variables can be categorical or continuous
Intent is to translate observations into a system that allows assessment of the hypothesis

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Nominal variables – assigns name or number purely on arbitrary basis (e.g., race, sex)
Ordinal variables – measures assigned from (typically) a lesser to greater value
Interval variables – scale that assigns a number to an observation based on a constant unit of measurement
Ratio – assigns numbers to observations to reflect a true point

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How is measure administered?
Has it been used on similar situations with success?
Is measure understandable by those being surveyed?
Is sample accessible and identifiable?
Is special training required?
What is length of time in measurement?
Are results available in timely manner?

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Reliability – the extent to which a measurement has stability and homogeneity
Validity – represents the precision to which the measure is truly measuring the phenomena being measured (measure must be reliable to be valid)

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Content Validity – the extent to which the measure reflects the full concept being studied
Criterion Validity – assessed by comparing the test measure of the phenomenon

 Sensitivity (Se) – measures how accurately the test identifies those with the condition or trait, i.e., correctly identifies or captures true positives
 High sensitivity is needed:
 When early treatment is important
 When identification of every case is important

 Specificity (Sp) – measures how accurately the test identifies those without the condition or trait, i.e., correctly identifies or excludes the true negatives.
 High specificity is needed when:
 Re-screening is impractical
 When reducing false positive is important

 Cost of false positives v. false negatives
 Importance of capturing all cases
 Likelihood population will be re-screened
 Prevalence of the disease (Pe): a.
Low Pe requires high Sp, otherwise too many false positives b.
High Pe requires high Se, otherwise too many false negatives

Disease State
Screening
Test Disease No Disease
Positive True Positive
(TP)
A
C
Negative False Neg.
(FN)
False Positive
(FN)
B
D
True Neg.
(TN)

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SE = TP / (TP + FN)
SP = Specificity = TN / (TN + FP)
False Neg. Rate = 1 – SE
False Pos. Rate = 1 - SP
Positive Predictive Value
TP/(TP + FP)
Negative Predictive Value
TN/(TN + FN)

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You need to test the validity of cervical smears – pap smears – to determine the presence of cancer of the cervix. Smears were taken from 120 women known to have cancer of the cervix and from 580 women who did not have cancer of the cervix. In the laboratory, the smears were read “blind” as positive or negative.
Of the total 700 smears, 200 were read as positive, 110 of which came from the proven cancer cases.

Theory
Empirical findings
Operational hypothesis
Statistical Tests Observations and measurements

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Cross-sectional – prevalence rates that may suggest association (good for developing theory, but no causal association)
Retrospective (Case-control) – good for rare diseases and initial etiologic studies
Prospective (cohort, longitudinal, followup) – yields incidence rates and estimates for risk. Better for causal association.
Experimental (intervention studies) – strongest evidence for etiology

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Stage of hypothesis development
Nature of disease
Nature of Exposure
Nature of study population
Context of research

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Single point in time (snapshot studies)
Risk factors and disease measured at the same time
Determines prevalence ratios

Sample
Population
Exposed
Non-
Exposed
Cases
Non-
Cases
Cases
Non-
Cases

Advantages and Disadvantages of
Cross Sectional Studies
Advantages
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Gives general description or scope of problem
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Useful in health service evaluation and planning
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Baseline for prospective study
Identifies cases and controls for retrospective study
Low-cost
Disadvantages
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No calculation of risk
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Temporal sequence is unclear
Not good for rare diseases
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Selective survival can lead to bias
Selective recall can lead to bias
Cohort effect may be misleading

Disease free persons are classified on exposure at beginning of follow-up period then tracked to ascertain the occurrence of disease.
Question of Study: Do persons with the factor of interest develop or avoid the disease more frequently than those without the factor or exposure

Sample
Population
Exposure
+
Exposure -
Cases
Non-
Cases
Cases
Non-
Cases

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Obtain Incidence data
Obtain the incidence among the exposed A/A+B
Obtain incidence among the nonexposed to determine relative risk
C/C+D
Determine Relative Risk
[A/(A+B)]/[C/(C+D)]

Advantages and Disadvantages of
Prospective Studies
Advantages
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Provides good assessment of temporal sequence
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Evaluate before onset of disease and watch for disease
Disadvantages
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Selection bias
Loss to follow-up
Expensive

Subjects are selected on the basis of disease status: either cases or controls then classified on the basis of past exposure
Question of Study: Do persons with the outcome of interest (cases) have the exposure characteristic (or history of exposure) more frequently than those without the outcomes (controls)

Exposure Positive A
Cases
Exposure Negative B
Exposure Positive C
Controls
Exposure Negative D

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Compare the odds of exposure among the cases with the odds of exposure among the controls
Odds of Exposure Among Cases =
[A/(A+C)]/[C/A+C)] or A/C
Odds of Exposure Among Controls
=[B/(B+D)]/[D/B+D)] or B/D
Get Odds Ratio or odds of expose among cases/Odds of exposure among controls
(A/C)/(B/D)

Advantages and Disadvantages of
Retrospective Studies
Advantages
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Less expensive than cohort (retrospective)
Studies
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Quicker than cohort
Can identify more than one exposure
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Good for rare diseases
Well design leads to good etiologic investigation
Disadvantages
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Selective Survival
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Selective recall
Temporal sequence not as clear
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Not suited for rare exposures
Gives an indirect measure of risk
More susceptible to bias
Limited to single outcome

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Uses an intervention in which the investigator manipulates a factor and measures the outcome
Elements of a complete experiment
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Manipulation of data
Use of a control group
Ability to randomize subjects to treatment groups

Advantages and Disadvantages of
Experimental Studies
Advantages
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Prospective direction
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Ability to randomize subjects
Temporal sequence of cause and effect
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Can control extraneous variables
Best evidence of causality
Disadvantages
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Contrive situation
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Impossible to control human behavior
Ethical Constraints
External validity uncertain
Expensive

The rate of disease in the exposed group attributable to exposure.
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Relative risk measures the strength of the association
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Attributable risk identifies risk of the disease attributable to exposure or the proportion of incidence in exposed group attributable to exposure

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Begins with (Incidence in the Exposed
Group) - (Incidence in the non-exposed group).
Search for the proportion of AR

Incidence in the
Exposed Group
-
Incidence in the
Exposed Group
Incidence in the Exposed Group

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Incidence rate of disease among those exposed to a trait or characteristic
Incidence rate of disease among those not exposed to the trait or characteristic
The proportion of the population that has the trait or characteristic

Incidence lung cancer, smokers
% of smokers in population
+
Inc. lung cancer, nonsmokers
% of nonsmokers in pop.
EXAMPLE Using NV Rates
[(28.0/1000)(.32)] + [(17.0/1000) (.68)] = 20.5

Incidence, total population
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Incidence, nonexposed population
EXAMPLE Using NV Rates
(20.5/1000) (17.0/1000) = 3.5/1000

Incidence, total population
-
Incidence, nonexposed pop.
Incidence in total population
EXAMPLE Using NV Rates
[(20.5/1000)- (17.0/1000)]
20.5
= 3.5/20.5 =
17%

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To demonstrate any therapeutic effect uses a PLACEBO
To demonstrate improved therapy compare to CONVENTIONAL
TREATMENT
To demonstrate the most effective regimen compare DIFFERENT
REGIMENS

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The importance of blinding depends on the needed outcome. Less important if the outcome is clear.
Non-blinded – both subject & investigator know the treatment allocation
Single-blinded – investigator knows, subject does not know
Double-blinded – neither investigator and subject

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During selection of participants
Absence of blinding allocation can lead to differential classification
Other sources of miscalculation
Withdrawals, ineligible sources, loss to follow-up
Premature termination

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Cases and controls, or exposed and nonexposed individuals were selected is such that an apparent association is observed even if there is no association.
Biased selection - taking from a pool in which we know the risk is higher is selection bias.
Small sample size or small response size

Methods of information about the subjects in the study are inadequate and results show information gathered regarding exposures and/or disease is incorrect.
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Reporting bias
Abstracting records
Bias in interviewing
Bias from surrogate interviews
Surveillance bias
Recall bias

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Confounding Variables
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To prove that Factor A is a result of disease B, we say that a third factor, Factor X is a
Confounder if the following is true:
Factor X is a known risk factor for Disease B.
Factor X is associated with Factor A bit is not a result of Factor A.
Interactions