Transcript Analytic Epidemiology - University of Nevada, Las Vegas

Analytic Epidemiology
Determining the
Etiology of Disease
Study Development Process
Descriptive Studies:
Data Collection and
Analysis
Analyze results and
retest
Model Building and
hypothesis
formulation
Analytic Studies for
Hypothesis testing
Causality and Causal
Relationships
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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)
Artifactual or Spurious
Associations
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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
Causal Association
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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Sources of Data
Primary data – information collected
directly by the researcher
 Secondary data – data that has
already been collected and stored
for analysis
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Types of Surveys
Administrative surveys, medical
records, vital records and statistical
data
 Telephone surveys
 Self-administered surveys
 Personal interviews
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Measurement Issues
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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Types of Categorical
Variables
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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
Improving the Survey
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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?
Reliability and Validity
Issues
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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Reliability and Validity
Issues
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
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Sensitivity
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:
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When early treatment is important
 When identification of every case is
important
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Specificity
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:
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Re-screening is impractical
 When reducing false positive is important
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Factors to consider in setting
cutoffs
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
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Disease State
Screening
Test
Disease
Positive
True Positive
(TP)
Negative
False Neg.
(FN)
No Disease
False Positive
(FN)
A B
C D
True Neg.
(TN)
Determining SE and SP
Rates
SE = TP / (TP + FN)
 SP = Specificity = TN / (TN + FP)
 False Neg. Rate = 1 – SE
 False Pos. Rate = 1 - SP
 Positive Predictive Value
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TP/(TP + FP)
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Negative Predictive Value
TN/(TN + FN)
SE and PE Example
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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.
The Research Cycle
Theory
Empirical findings
Statistical Tests
Operational
hypothesis
Observations and
measurements
Types of Studies
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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
Considerations for Study
Design
Stage of hypothesis development
 Nature of disease
 Nature of Exposure
 Nature of study population
 Context of research
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Cross-Sectional Studies
Single point in time (snapshot
studies)
 Risk factors and disease measured
at the same time
 Determines prevalence ratios
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Cross-Sectional Study
Design
Exposed
Cases
NonCases
Sample
Population
Cases
NonExposed
NonCases
Advantages and Disadvantages of
Cross Sectional Studies
Advantages
 Gives general
description or scope
of problem
 Useful in health
service evaluation and
planning
 Baseline for
prospective study
 Identifies cases and
controls for
retrospective study
 Low-cost
Disadvantages
 No calculation of risk
 Temporal sequence is
unclear
 Not good for rare
diseases
 Selective survival can
lead to bias
 Selective recall can
lead to bias
 Cohort effect may be
misleading
Prospective Study Desgin
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
Prospective Study Design
Exposure
+
Sample
Population
Cases
NonCases
Cases
Exposure NonCases
Prospective Study
Criteria
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)]
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Advantages and Disadvantages of
Prospective Studies
Advantages
 Provides good
assessment of
temporal sequence
 Evaluate before
onset of disease
and watch for
disease
Disadvantages
 Selection bias
 Loss to follow-up
 Expensive
Retrospective Study
Design
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)
Retrospective Study Design
Exposure Positive
A
Exposure Negative
B
Exposure Positive
C
Exposure Negative
D
Cases
Controls
Retrospective Study Method
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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
 Less expensive than
cohort (retrospective)
Studies
 Quicker than cohort
 Can identify more
than one exposure
 Good for rare
diseases
 Well design leads to
good etiologic
investigation
Disadvantages
 Selective Survival
 Selective recall
 Temporal sequence
not as clear
 Not suited for rare
exposures
 Gives an indirect
measure of risk
 More susceptible to
bias
 Limited to single
outcome
Experimental Studies
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
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Advantages and Disadvantages of
Experimental Studies
Advantages
 Prospective direction
 Ability to randomize
subjects
 Temporal sequence of
cause and effect
 Can control
extraneous variables
 Best evidence of
causality
Disadvantages
 Contrive situation
 Impossible to
control human
behavior
 Ethical Constraints
 External validity
uncertain
 Expensive
Attributable Risk
The rate of disease in the exposed group
attributable to exposure.
 Relative risk measures the strength of the
association
 Attributable risk identifies risk of the
disease attributable to exposure or the
proportion of incidence in exposed group
attributable to exposure
Attributable Risk
Calculation
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Begins with (Incidence in the Exposed
Group) - (Incidence in the non-exposed
group).
Search for the proportion of AR
Attributable Risk
Calculation
Incidence in the
Incidence in the
Exposed Group - Exposed Group
Incidence in the Exposed Group
Population Attributable Risk
Requirements
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
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PAR Example
Incidence
% of
Inc. lung
lung cancer, smokers in
+ cancer, nonsmokers
population
smokers
% of nonsmokers in
pop.
EXAMPLE Using NV Rates
[(28.0/1000)(.32)] + [(17.0/1000) (.68)] = 20.5
Attributable Risk Example
Incidence, total
population
- Incidence, non-
exposed population
EXAMPLE Using NV Rates
(20.5/1000) -
(17.0/1000) = 3.5/1000
PAR
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%
Intervention Comparisons
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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Blinding in Experimental
Studies
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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
Sources of Bias
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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Selection Bias
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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
Information Bias
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
Other Issues
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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