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8/22/2019 Confounding and Validity 2009
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What is the odds of getting pancreaticcancer among coffee drinkers?
Coffee
drinking
Pancreatic
cancer
Smoking
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What is the odds of having a Downsyndrome child in higher parity?
Parity Down syndrome
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0
1
2
3
4
5
OR of Down syndrome
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Parity Down syndrome
Age
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Exposure of Interest
Precursors
Mechanisms
Health Outcome
Confoundersestimateassociation
causalinferenceeasure
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A situation in which effects of two riskfactors are mixed in the occurrence of thehealth problem under study May lead to
overestimation or under-estimationof the true association between exposureand outcome
Can change the direction of the observedeffect
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Must be a risk factor for the disease among
unexposed; do not have to be a true cause of
disease.
Must be associated with the exposure under study inthe source population.
The confounder cannot be an intermediate step in
the causal path between the exposure and the
disease.
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Exposure Case ControlPresent 30 18
Absent 70 82
Total 100 100
OR= 30 . 82 = 1.9570 . 18
Case and control are not matched
Is this association a causal one or could ithave resulted from differences in agedistribution?
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Age (yr) Case Control40 50 20
Total 100 100
Older age is associated with being a case
Is age related to the degree of exposure?
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Age (yr) Total Exposed Not exposed % exposed40 70 35 35 50
Age is related to exposureAge is related to being a case
Is the association between exposure and disease causal?Or caused by the age differences?
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Age (yr) Exposed Cases Control OR 40 + 25 10 25 . 10- 25 10 25 . 10
Total 50 20 = 1.0
The OR = 1.94 was because of the differencein age distribution between case and control
Age is a confounder
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In designing ad carrying out a study Matching (individual or group)
In the analysis of data Stratification
Adjustment
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Confounder
Exposure Outcome
Non-causal
Causal
Intermediate factor
Exposure Outcome
Ex : high fat diet obesity coronary heart disease
Ex : high fat diet DM coronary heart disease
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Diet/lifestyle
Vitamin C Cancer
Non-causalCausal
Case-control study to determine whether vitamin C intakeis associated with colon cancer.
People who take vitamin C may eat a healthierdiet and live a healthier lifestyle
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Interpreting data requires assumptions
about causal relations (including what
factors are potential confounders, i.e., whatfactors affect incidence and are not
themselves caused by the exposure).
If exposed people and unexposed people
differ on factors that affect disease
incidence, then those factors may confound
(distort) the observed relation betweenexposure and disease (i.e., actualconfounding).
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We can control confounding by studydesign if we can make the exposed andunexposed groups similar in respect to alldisease determinants, though matching orrandomized assignment of exposure
We can control confounding in the analysis
if we can stratify the data by disease
determinants that are not themselvescaused by the exposure (i.e., not causal
intermediates).
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VALIDITY
ANDRELIABILITY
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What is the prevalence of coronaryarterial disease among post-graduate
students of UNPAD?
How will you conduct the research?
Design?
Measurement?
Result?
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Susceptiblehost
Subclinicaldisease
Clinicaldisease
Recoverycondition,
disability ordeath
Point ofexposure
Onset ofSymptoms
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Population
Test -ve Test +ve
Unaffected
Re-test
Affected
intervene
Screening
Clinical exam
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The degree to which a
measurement or study reaches a
correct conclusion ~accuracy
The observed measurements will
be compared with accepted(gold) standard
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Internal validity
The degree to which the observed results of the studyare true
Inferences are correct regarding the participants in the
study
External Validity
Generalizability of the result
Inferences are correct regarding the population at risk
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POPULATION
SAMPLE SAMPLE
Selection bias
CONCLUSION
Measurement bias &
confounding
Chance
Internal validity
External validity(generalizability)
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The observed results(conclusion) occurredbecause:
Chance
Random error Bias
Systematic error
Confounding Truth
IF: the role of chance is small
bias can be reasonablyexcluded
confounding is addressed
THENthe study is internally valid
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Content validity: Measurement includes all the dimension
Construct validity: Measurement is related in a coherent way
Criterion validity: Measurement predict a directly observable
phenomenon
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Consistency of Measurement Reproducibility over time
Consistency between different
coders/observers Consistency among multiple indicators
Estimates of Reliability
Statistical coefficients that tell use how
consistently we measured something
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1. Stability Consistency across time: repeat measurements
2. Reproducibility Consistency between observer
3. Homogeneity Consistency between different measures of the
same concept: use different items to get aconclusion of the same concept
4. Accuracy Lack of mistakes in measurement: god concept of
definition and procedures Dedicated observers: training, motivation,
concentration
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Reliability is a necessary condition for validity If it is not reliable it cannot be valid
Reliability is NOT a sufficient condition for
validity If it is reliable it may not necessarily be valid
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Reliable BUT NOT Valid
Reliable AND valid
Not reliable, nor valid
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