1. Epidemiology Kept Simple
Chapter 12
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Epidemiology Kept Simple
Chapter 12
Error in Epidemiologic Research
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Error in Epidemiologic Research

2. §12.1 Introduction
View analytic studies as exercises in measuring association between exposure & disease
All such measurements are prone to error
We must understand the nature of measurement error if we are to avoid train wrecks*
* a disaster or failure, especially one that is unstoppable or unavoidable
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3. Parameters and Estimates
Understanding measurement error begins by distinguishing between parameters and estimates
Parameters
Error-free
Hypothetical
Measure effect
Statistical estimates
Error-prone
Calculated
Measure association
Epidemiologic statistics are mere estimates of the parameters they wish to estimate.
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4. Target Analogy
Let Φ represent the true relative risk for an exposure and disease in a population
Replicate the study many times
How closely does an estimate from a given study reflect the true relative risk (parameter)?
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5. Target Analogy
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6. Random Error (Imprecision)
Balanced “scattering”
To address random error, use
confidence intervals
hypothesis tests of statistical significance
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7. Confidence Intervals (CI)
Surround estimate with margin of error
Locates parameter with given level of confidence (e.g., 95% confidence)
CI width quantifies precision of the estimate (narrow CI  precise estimate)
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8. Mammagraphic Screening and Breast Cancer Mortality
Exposure ≡ mammographic screening
Disease ≡ Breast cancer mortality
First series (studies 1 – 10): women in their 40s
Second series (studies 11 – 15): women in their 50s
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9. Hypothesis Tests
Start with this null hypothesis H0: RR = 1 (no association)
Use data to calculate a test statistic
Use the test statistic to derive a P-value
Definition: P-value ≡ probability of the data, or data more extreme, assuming H0 is true
Interpret P-value as a measure of evidence against H0 (small P gives one reason to doubt H0)
R.A. Fisher
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10. Conventions for Interpretation
Small P-value provide strong evidence against H0 (with respect to random error explanations only!)
By convention
P ≤ .10 is considered marginally significant evidence against H0
P ≤ .01 is considered highly significant evidence against H0
Do NOT use arbitrary cutoffs (“surely, god loves P = .06”)
R.A. Fisher
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11. Childhood SES and Stroke
Factor RR
P-value
Crowding (persons/room)
< 1.5 = – 2.49 = 1.0 (ref.) 2.5 – 3.49 = 0.6  3.5 = 1.0
trend P = 0.53
Tap water
0.73
P = 0.53
Toilet type
flush/not shared = 1.3 flush/shared = 1.0 (referent) no flush = 1.0
trend P = 0.67
Ventilation
good = 1.0 (ref.) fair = 1.7 poor = 1.7
trend P = 0.08
Cleanliness
good= 1.1 fair = 1.0 (ref.) poor = 0.5
trend P = 0.07
Nonsignif. evidence against H0
Marginally significant evidence against H0
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Source: Galobardes et al., Epidemiologic Reviews, 2004, p. 14

12. Systematic Error (Bias)
Bias = systematic error in inference (not an imputation of prejudice)
Amount of bias
Direction of bias
Toward the null (under-estimates risks or benefits)
Away from null (over-estimates risks or benefits)
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13. Categories of Bias
Selection bias: participants selected in a such a way as to favor a certain outcome
Information bias: misinformation favoring a particular outcome
Confounding: extraneous factors unbalanced in groups, favoring a certain outcome
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14. Examples of Selection Bias
Berkson’s bias
Prevalence-incidence bias
Publicity bias
Healthy worker effect
Convenience sample bias
Read about it: pp. 229 – 231
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15. Examples of Information Bias
Recall bias
Diagnostic suspicion bias
Obsequiousness bias
Clever Hans effect
Read about it on page 231
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16. Memory Bias Memory is constructed rather than played back
Example of a memory bias is The Misinformation Effect ≡ a memory bias that occurs when misinformation affects people's reports of their own memory
False presuppositions, such as "Did the car stop at the stop sign?" when in fact it was a yield sign, will cause people to misreport actually happended
Loftus, E. F. & Palmer, J. C. (1974). Reconstruction of automobile destruction. Journal of Verbal Learning and Verbal Behaviour, 13,
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17. Recall Bias in Case-Control Studies
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18. Differential & Nondifferential Misclassification
Non-differential misclassification: groups equally misclassified
Differential misclassification: groups misclassified unequally
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19. Nondifferential and Differential Misclassification Illustrations
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20. Confounding A distortion brought about by extraneous variables
From the Latin meaning “to mix together”
The effects of the exposure gets mixed with the effects of extraneous determinants
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21. Properties of a Confounding Variable
Associated with the exposure
An independent risk factor
Not in causal pathway
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22. Example of Confounding
E ≡ Evacuation method
D ≡ Death following auto accident
Died
Survived
Total
Helicopter 64
136
200
Road
260
840
1100
R1 = 64 / 200 = .3200
R0 = 260 / 1100 = .2364
RR = / = 1.35
Does helicopter evacuation really increase the risk of death by 35%? Surely you’re joking!
Or is the RR of 1.35 confounded?
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23. Serious Accidents Only (Stratum 1)
Died
Survived
Total
Helicopter 48 52
100
Road 60 40
R1 = 48 / 100 = .4800
R0 = 60 / 100 = .6000
RR1 = .48 / .60 = 0.80
Helicopter evacuation cut down on risk of death by 20% among serious accidents
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24. Minor Accidents Only (Stratum 2)
Died
Survived
Total
Helicopter 16 84
100
Road
200
800
1000
R1 = 16 / 100 = .16
R0 = 200 / 1000 = .20
RR2 =.16 / .20 = 0.80
Helicopter evacuation cut down on risk of death by 20% among minor accidents
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25. Accident Evacuation Properties of Confounding
Seriousness of accident (Confounder) associated with method of evacuation (Exposure)
Seriousness of accident (Confounder) is an independent risk factor for death (Disease)
Seriousness of accident (Confounder) not in causal pathway between helicopter evaluation (Exposure) and death (Disease)
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