Epid 600 Class 8 Bias

8/14/2019 Epid 600 Class 8 Bias

1/51

EPID 600; Class 8Bias

University of Michigan School of Public Health

1


2/51

Bias

Systematic error in the design, conduct or analysis of a

study that results in a mistaken estimate of an exposures

effect on disease

2


3/51

Bias



effect on disease

Wrong study design!Wrong sampling strategy!

3


4/51

Bias



effect on disease

Problems in enrollment of cases, of controls!Loss to follow-up!

Poor collection of data!

4


5/51

Bias

Systematic error in the design, conduct oranalysis of a


effect on disease

Wrong modeling assumptions!

Miscategorization of variables!

5


6/51

Rothman KJ. Epidemiology: An Introduction. Oxford, 2002. 6


7/51

Evaluating bias

1. Why did it occur?2. What effect does it have on the observed association?3. What can be done to control for bias in this study and to

prevent it in future studies?

7


8/51

Types of (important) bias

1. Selection biasError in selection of study participants

2. Information biasErrors in procedures for gathering relevant information

8


9/51

1. Selection bias

Systematic error in selecting subjects into one or more of

the study groups, such as cases and controls, or exposed

and unexposed

9


10/51

Study question

Does coffee drinking cause pancreatic cancer?

10


11/51

Selection Bias: in a case-control study

Cases: patients hospitalized with a diagnosis of

pancreatic cancer

Controls: patients hospitalized for other reasons by the

same gastroenterologist who had hospitalized the case

Results: found a strong relationship between coffee

drinking and pancreatic cancer

11


12/51

What happened?

POPULATION

Yes No

Yes

NoCoffee

Cancer

Yes No

Coffee

Persons who do not drink

coffee are more likely to be

controls

Cancer

Yes

No

STUDY SAMPLE

12


13/51

Study question

Is there a relation between occupational exposure to

asbestos and lung cancer?

13


14/51

Selection Bias: in a cohort study

Exposed: workers who handle asbestos (100%

participation)

Unexposed: workers in other areas of the factory who

agree to participate

(50% participation)

Results: found NO relationship between asbestos and

lung cancer

14


15/51

What happened?

POPULATION

Yes No

Yes

No

Asbestos

Cancer

Yes No

UNEXPOSED workers who

participate are those at high risk

for lung cancer, so unexposed

with disease are over-

represented

Cancer

No

STUDY SAMPLEAsbestos

Yes

15


16/51

2. Information Bias

Systematic error in obtaining information regarding

subjects in the study

Examples: bias in recall, in collecting data, in interview,in reporting

16


17/51

Study question

Is perinatal infection associated with a risk of congenital

malformation?

17


18/51

Information Bias in a case-control study:

Example 1

Cases: newborns with congenital malformations

Controls: healthy newborns

Results: found a strong relationship between mothers

recall of infection during pregnancy and malformation

18


19/51

What happened?

Recall bias

Parents of children with congenital malformations were

more likely to report infection during pregnancy thanparents of children without congenital malformations

19


20/51

What happened?

POPULATION

Yes No

Yes

No

Yes No

Infection

during

pregnancy

Yes

No

Infection

during

pregnancy

Congenital

Malformation

Congenital

Malformation

STUDY SAMPLE20


21/51

What happened?

POPULATION

Yes No

Yes

No

Yes No

Infection

during

pregnancy

Yes

No

Infection

during

pregnancy

Congenital

Malformation

Congenital

Malformation

Misclassification of unexposed as

exposed is more common in cases

than in controls DIFFERENTIAL

MISCLASSIFICATION

STUDY SAMPLE21


22/51

What happened?

POPULATION

Yes No

Yes

No

Yes No

Infection

during

pregnancy

Yes

No

Infection

during

pregnancy

Congenital

Malformation

Congenital

Malformation



than in controls DIFFERENTIALMISCLASSIFICATION

STUDY SAMPLE22


23/51

What if there is misclassification and it

is similar in both cases and controls ?

Case Non-Case

InfectionYes

No

Non-differential misclassification

Usually biases estimate of association towards 1 (the null)

23


24/51

Toward the null

1

0.5

2

3

0

the null

24


25/51

Study question

Is smoking associated with an increased risk of myocardial

infarction (MI) ?

25


26/51

Information Bias in a case-control study:

Example 2

Cases: hospitalized cases of MI in elderly adults

Controls: elderly adults, randomly selected from the

community, who have never been hospitalized for MI

Results: found a weak relationship between smoking and

MI

26


27/51

What happened?

Many true cases of MI are misclassified as non-cases, and

are included in the controls (they were not hospitalized and

had no symptoms)

27


28/51

What happened?

POPULATION

Yes No

Yes

No

Yes No

Smoke Yes

No

Smoke

Myocardial

Infarction

Myocardial

Infarction

Misclassification of cases as controls

is similar in smokers and non-

smokers NON-DIFFERENTIALMISCLASSIFICATION

STUDY SAMPLE28


29/51

What happened?

POPULATION

Yes No

Yes

No

Yes No

Smoke Yes

No

Smoke

Myocardial

Infarction

Myocardial

Infarction

Misclassification of cases as

controls is similar in smokers and

non-smokers NON-DIFFERENTIAL

MISCLASSIFICATION

STUDY SAMPLE29


30/51

What happened?

POPULATION

Yes No

Yes

No

Yes No

Smoke Yes

No

Smoke

Myocardial

Infarction

Myocardial

Infarction

Misclassification of cases as

controls is similar in smokers and

non-smokers NON-DIFFERENTIAL

MISCLASSIFICATION

STUDY SAMPLE30


31/51

Study question

Is use of oral contraceptives (OC) associated with an

increased risk of venous thrombophlebitis (blood clots)?

31


32/51

Information Bias: in a cohort study

Exposed: women who use OC

Unexposed: women who do not use OC

Results: found a strong relationship between OC use

and thrombophlebitis

32


33/51

What happened?

Detection bias (also called surveillance bias)

Women who are on oral contraceptives are more likely to

receive a diagnosis of thrombophlebitis

33


34/51

What happened?

POPULATION

Yes No

Yes

No

Yes No

OC

Use

Yes

No

OC

Use

Thrombophlebitis

STUDY SAMPLE

Thrombophlebitis

34


35/51

What happened?

POPULATION

Yes No

Yes

No

Yes No

OC

Use

Yes

No

OC

Use

Thrombophlebitis

Misclassification of non-disease as

disease is different in exposed

and unexposed persons

DIFFERENTIAL

MISCLASSIFICATION

STUDY SAMPLE

Thrombophlebitis

35


36/51

What happened?

POPULATION

Yes No

Yes

No

Yes No

OC

Use

Yes

No

OC

Use

Thrombophlebitis



and unexposed persons

DIFFERENTIAL

MISCLASSIFICATION

STUDY SAMPLE

Thrombophlebitis

36


37/51

Putting numbers to the differential vs. non-

differential examples, 1

POPULATION

Yes No

Yes

No

Yes No

OC

Use

Yes

No

OC

Use

Thrombophlebitis



and unexposed personsDIFFERENTIAL MISCLASSIFICATION

RESULTING IN BIAS AWAY FROM

THE NULL

STUDY SAMPLE

Thrombophlebitis

50

50 100

25

70

50 100

5

REAL OR =

(100*50)/

(50*25)=4

BIASED OR =

(100*70)/

(50*5)=2837


38/51



POPULATION

Yes No

Yes

No

Yes No

Infection

during

pregnancy

Yes

No

Infection

during

pregnancy

Congenital

Malformation

Congenital

Malformation




MISCLASSIFICATION RESULTINGIN BIAS AWAY FROM THE NULL

STUDY SAMPLE

50

50 100

25

REAL OR =

(100*50)/

(50*25)=4

75

25 100

25

BIASED OR =

(100*75)/

(25*25)=1238


39/51

25

75



POPULATION

Yes No

Yes

No

Yes No

Exposure

Yes

No

Exposure

Disease

Disease

Misclassification of exposed as

unexposed is more common in cases


MISCLASSIFICATION RESULTING IN

BIAS TOWARDS THE NULL

STUDY SAMPLE

50

50 100

25

REAL OR =

(100*50)/

(50*25)=4100

25

BIASED OR =

(100*25)/

(25*75)=1.339


40/51


41/51

Accuracy of weight/height reports

Obesity is acknowledged as a critical health probleminternationally

Studies often use reported (as opposed to measured) data to

estimate the prevalence of overweight and obesity at the

population levelThere have been investigations regarding the truth of these

reported values in adults and adolescents; the validity of

parent-reported weight and height was studied by a team in

Canada.

Dubois and Girad. Accuracy of maternal reports of pre-schoolers weights and heights as estimates of BMI values. Int J Epid. 2007; 36: 132-138.

41


42/51

Height/weight reports

2) Within 3 months,

childrens weight

and height were

directly measured

1) Mothers asked to

report on height and

weight of children

aged 4

3) Investigators

examined the

prevalence of

obesity based on

reported valuesversus

prevalence of

obesity based on

measured values


42


43/51


The cohort: 4-year old children in 2002, who were part of a regional

stratified sample of children born in Quebec in 1998

Height/Weight report: One care-giver, usually the mother, reported height

and weight to an interviewer; the caregiver was not told that subsequent

measurement would be taken.

Interviewers made sure that mothers recalled these values rather than

measuring them on the spot

Height and weight measurement: Within three months of the interview,

nutritionists followed a standardized protocol and measured height and

weight of children


43


44/51

Height/weight report: is it the same for

all?

SES Reported Measured

Highest 1 1

Middle 1.8 1.7

Lowest 2.2 1.9

Odds ratios among boys:

BMI>95th Percentile

Is any group of people

consistently over-

reporting BMI of children?


44


45/51


In this figure, the measuredweight is 17 kg for a 51-

month-old child who is 1.03m

tall. This child ranks at the

71st percentile if the child is a

girl and at the 65th percentile if

the child is a boy.

If the mother reports the

weight as being 2 kg less than

the actual value, the child

would be classified as beingbelow the 15th percentile.


45


46/51

Height/weight report: findings

Heights were reported more accurately than weights (there was no

difference in the means of reported vs. measured heights)

A greater proportion of mothers overestimated boys weights; a

greater proportion of lower SES mothers misreport

12% of the children were classified as overweight based on thereported data; 9% were classified as overweight using measured

data 3% overestimation of overweight in this population


46


47/51

Special biases

Non-respondent bias

Persons who do not participate in a particular study may be

different than those who do

e.g., in telephone surveys, women are more likely to

answer surveys than are men; if the exposure of interest is

differentially distributed between women and men and if

gender is associated with the outcome of interest bias willresult

47


48/51

Other special biases

Unmasking (detection signal) bias

Membership bias

Diagnostic suspicion bias

Exposure suspicion biasRecall bias

Family information bias

Neyman bias

Berkson bias

etc

48


49/51

Evaluating Bias

1. Why did it occur?2. What effect does it have on the observed association?3. What can be done to control for bias in this study, and

to prevent it in future studies?

49


50/51

Preventing Bias

Careful attention to sampling

Minimize non-response

Standardization of measurements

Training and quality controlBlinding

50


51/51

51

Epid 600 Class 8 Bias

Documents

Transcript of Epid 600 Class 8 Bias