1. Validity Generalization
Hein Stigum
Presentation, data and programs at:
Sep-18
H.S.

2. Precision and validity
Measures of populations
precision - random error - statistics
validity - systematic error – epidemiology
Lack of validity = measures are biased
type of bias
direction of the bias
True
value
Estimate
Precision
Bias
Random error: study samples, p-values and confidence intervals
Systematic error: largely observational studies, little control
Freq measures (incidence and prevalence) and association measures (RR, OR, RD)
The measures are biased (not the study): type, direction
Example CC: prevalence is biased, OR is correct
Sep-18
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3. Type of bias Selection bias Information bias Confounding
Are those who answer different?
Information bias
Do they tell the truth?
Confounding
Is the association a cause?
def. of main types
distortion of estimate ...
Ex: If you send out a questionairre to study illiteracy, subject who cannot write will not answer, and we have a serious selection bias.
Information bias ...
Ex: If you want to study the effect of passiv smoking on astma, the exposure to passiv smoking is difficult to measure,...
Recall events in the past
Confounding…
ecological study of obesity and diabetes by contry, many other factors that differ. Comparison bias.
Also in individ bases designs, example later
Classification not alway clear
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4. Selection bias
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5. Sources of selection bias
Selective response
sexual survey
Self selection
Nevada atom test and leukemia
Loss to follow up connected to disease
air pollution and astma
Healthy worker effect
aluminium workers and lung disease
Selective response
sexual survey, never sex: no response. Show next 2 slides
All designs
Cohort
Self selection:
Nevada atom test and leukemia, soldiers exposed , 20% self selected, 80% of the exposed sample traced by investigators, cases counted, 4 times as much leukemia among self selected. Internet surveys is another example
Loss to follow up not random:
air pollution and asthma, worst cases move to less polluted areas, lost , lower incidence, less RR
In general: if loss is connected to disease, get bias
Selective survival. Show slide +3 after this
Study effect of work exposures: heat and dust i Norw aluminium and disease, less disease among heavy exposed worker. Disease self-selceted out, healthy worker effect.
Cross, CC with prevalent cases
Case-control,
Cases:
Ex: HTLV-and leukemia/neurological disorder (ATL, TSP), death after y disease. If select
prevalent cases, most severe case may have died, selective survival
Controls:
Controls from a hospital (without the study disease), get biases results. Controls should be from the same population as the cases.
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6. Selection bias Population Sample Respons Responders Non-responders
If non-respone is random, OK
If non-respone is connected to outcome, get bias
(Go back)
Outcome
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7. Selective survival Disease Death Sep-18 H.S.
Exposure at time 0. Start study at time 10
If we do our study CC on prevalent cases, get only the the two lower ones
Use incident cases instead
Go back 3 slides
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8. Information bias
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9. Non-differential misclassification
True smoking
10% of smokers report
no smoking
Non-differential: RR
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10. Sources of information bias
Not true
males report more partners than females
Not blinded
passive smoking and astma
Selective recall
alcohol in pregnancy and malformations
Wrong info
sex surveys partners: recall or wrong information
Not blinded
Cohort: asthma unclear, doctors not blinded to exposure may lead to differential misclass
CC: passiv smoking unclear, experimenter not blinded to disease may lead to differential misclass
Selective recall
CC: recall: mothers with malformed babies remember more alcohol use, differential misclass
information bias depends on data source: registry, doctors diagnosis, laboratory test, self report interview or questionnaire
A bias in the information may lead to a misclassification of disease status or exposure status or both.
2 types of measures:
Freq Ex: if we measure prev of a disease and we know that doctors do not find all cases, then some cases are misclassified, we say that our prev measure is biased (toward lower value)
Unclear diagnosis, asthma,.
For assoc. we may have ..
an important consideration is connected to exp or dis.
Assoc.Ex: asthma and air pollution, assume no association, doctors know who are exposed, then the exposed may be examined more carefully than unexposed. This would be a differential miscl. Therefore doctors should be “blinded” to exposure status.
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11. CONFOUNDER DEFINITION
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12. Confounding Ideal: Practice: Comparison bias:
Same subjects are both exposed and unexposed at the same time, (counterfactual)
Practice:
As equal as possible
Comparison bias:
Confounding
Exposed
Unexposed
General definition
As equal as possible, except for exposure
Now focus on one spesific confounder
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13. Associations E and D associated E causes D E and D have common cause
Both
Overall E-D association = spurious effect from C causal E-D effect E D E D C
3 ways to be associated (show 2 here):
Smoking causes lung cancer
Yellow fingers and lung cancer are associated from common cause smoking
Remove spurious effect by stratify, adjust or condition on C E D C
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14. Classic confounder C is the confounder RRED is biased
RRED|C is unbiased
RRED =0.8 positive bias
Adjust for age:
RRED|C=0.5 is unbiased E D C
birth defects
vitamins
age + -
true
biased 1 
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15. Confounding: Downs syndrom by parity%
Confounding, ex
Downs (mongoloism), reduced mental capacity, birth registry, prev=12/10 000=0.1%
Parity=number of previous births
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16. Downs by parity and mothers age%
Stratify on mothers age
No effect of parity, large effect og age
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17. Downs syndrom, logistic regression
Crude
Adjusted
Mod 1 unadjusted (crude) (rare disease: 1/1000 )
Mod 2 adjusted
Same adjustment in linear models
Confounding, example of systematic error->bias
Example of estimation (rather than testing)
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18. Generalization
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19. Generalization Do the result apply outside the sample?
Statistical generalization
Smoking among males, generalize to females?
Representative sample
Biological generalization
Drug effect on males, generalize to females?
Information from outside the study
Animal studies, generalize to humans?
Homogenous sample
Convinced ourselves that the results are valid, do they apply outside the sample.
Generalize from males to females:
Prevalence of smoking No
Association smoking-early sexual debut Probably not
Drug effect Yes, if information on metabolic pathway
Stronger results if homogenous sample
Do hamsters need to be representative?
Two conflicting ways: representative will not be homogenous
Sep-18
H.S.