bias in epidemiology wenjie yang ywjie@zzu.edu.cn 2007.12
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“The search for subtle links between diet, lifestyle, or environmental factors and disease is an unending source of fear but often yields little certainty.”
____Epidemiology faces its limits.
Science 1995; 269: 164-169.
What can be wrong in the study?
Random error
Results in low precision of the epidemiological measure measure is not precise, but true
1 Imprecise measuring
2 Too small groups
Systematic errors(= bias)
Results in low validity of the epidemiological measure measure is not true
1 Selection bias
2 Information bias
3 Confounding
Random error
• Low precision because of– Imprecise measuring– Too small groups
• Decreases with increasing group size
• Can be quantified by confidence interval
Bias in epidemiology1 Concept of bias
2 Classification and controlling of bias
2.1 selective bias
2.2 information bias
2.3 confounding bias
Random error :
Definition
Deviation of results and inferences
from the truth, occurring only as a
result of the operation of chance.
2 Classification and controlling of bias
Assembling subjects
collecting data
analyzing data
Selection bias
Information bias
Confounding bias
Time
VALIDITY OF EPIDEMIOLOGIC STUDIES
Reference Population
Study Population
External Validity
Exposed UnexposedInternal Validity
2.1 Selection bias2.1.1 definition
Due to improper assembling method or limitation, research population can not represent the situation of target population, and deviation arise from it.
2.1.2 several common Selection biases
( 1 ) Admission bias ( Berkson’s bias)
There are 50,000 male citizen aged 30-50 years old in a community. The prevalence of hypertension and skin cancer are considerably high. Researcher A want to know whether hypertension is a risk factor of lung cancer and conduct a case-control study in the community .
case control sum
Hypertension 1000 9000 10000
No hypertension 4000 36000 40000
sum 5000 45000 50000 χ2 =0
OR=(1000×36000)/(9000 ×4000)=1
Researcher B conduct another case-control study in hospital of the community.(chronic gastritis patients as control) .
admission rate
Lung cancer & hypertension 20%
Lung cancer without hypertension 20%
chronic gastritis & hypertension 20%
chronic gastritis without hypertension 20%
case control sum
hypertension 200 (1000) 200 (2000) 400
No hypertension 800 (4000) 400 (8000) 1200
sum 1000 (5000) 600 (10000) 1600
case control sum hypertention 40 100 140
No hypertention 160 200 360
sum 200 300 500
χ2 =10.58 P<0.01
OR=(40×200)/(100×160)=0.5
Abortion
Yes No
1 2
2 1
number of subjects:N
proportion of red ball:A
numbers who’s answer is “1”:K
Abortion rate: X
Abortion
Yes No
1 2
2 1
number of subjects:N=1000
proportion of red ball:A=40%
numbers who’s answer is “1”:K=540
Abortion rate: X=?
N*A *X+ N*(1-A) *(1-X)=K
2.3 Confounding bias
Definition:
The apparent effect of the exposure of interest is distorted because the effect of an extraneous factor is mistaken for or mixed with the actual exposure effect.
Properties of a Confounder:
• A confounding factor must be a risk factor for the disease.
• The confounding factor must be associated with the exposure under study in the source population.
• A confounding factor must not be affected by the exposure or the disease.
The confounder cannot be an intermediate step in the causal path between the exposure and the disease.
2 In analysis phase
1) Stratified analysis (Mantal-Hazenszel’s method)2) Standardized
3) logistic analysis
A case-control study of Oral contraceptive to myocardial infarction
OC MI control sum + 29 135 164
- 205 1607 1812
sum 234 1742 1976 χ2 =5.84 ,P<0.05 cOR=1.68 OR 95C.I.(1.10,2.56)
Age distribution in 2 group
age ( year ) MI proportion ( % ) case proportion ( % ) OR
25~ 6 2.6 286 16.4 1.0 30~ 21 9.0 423 24.3 2.36 35~ 37 15.8 356 20.4 4.95 40~ 71 30.3 371 21.3 9.12 45~49 99 42.3 306 17.6 15.42 合计 234 100.0 1742 100.0 ----
OC exposure proportion in different age groups( % )
OC exposure in MI Age
( year ) + - sum
exposure
Proportion(%)
OC exposure in control
+ - sum exposure
Proportion(%)
25~ 4 2 6 66.7 62 224 286 21.7
30~ 9 12 21 42.9 33 390 423 7.8
35~ 4 33 37 10.8 26 330 356 7.3
40~ 6 65 71 8.5 9 362 371 2.4
45~49 6 93 99 6.1 5 301 306 1.6
sum 29 205 234 12.4 135 1607 1742 7.7
χ2 =38.99 P<0.01 χ2 =108.43 P<0.01
Stratified analysisage ( year ) OC MI Control OR
25~ + 4 62
- 2 224
OR95%C.I.
7.2 (1.64,31.65)
30~ + 9 33
- 12 390 8.9 (3.96,19.98)
35~ + 4 26
- 33 330 1.5 (0.53,4.24)
40~ + 6 9
- 65 362 3.7 (1.36,10.04)
45~49 + 6 5
- 93 3013.9 (1.26,12.10)
Analytic epidemiology :Case-control study; HIV “carried” by
mosquitoes ?
175HIV +
390Controls
Mosquito exposure No exposure
565
158 17
247 143
405 160O.R. = 5.38
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