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HSS4303B – Intro to Epidemiology Feb 11, 2010

HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

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Page 1: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

HSS4303B – Intro to Epidemiology

Feb 11, 2010

Page 2: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Judge Yes They Are Hot No They Are Not Totals

Yes They Are Hot 41 3 44

No They Are Not 4 27 31

Totals 45 30 75

Pr(a) = relative observed agreement = (41 + 27 )/ 75 = 90.7%

Hasselhoff’s Responses

Shat

ner’s

Res

pons

es

Page 3: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Pr(a) = relative observed agreement = (41 + 27 )/ 75 = 90.7%

Pr(e) = prob that agreement is due to chance =

(44x45/752 + (31x30)/752 = 0.352 + 0.165 = 51.7%

(multiply marginals and divide by total squared)

Judge Yes They Are Hot No They Are Not Totals

Yes They Are Hot 41 3 44

No They Are Not 4 27 31

Totals 45 30 75

Shat

ner’s

Res

pons

es

Hasselhoff’s Responses

Page 4: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

What Have We Done So Far?

• Morbidity & mortality• Risk• Natural history of disease• Kaplan-Meier and Life Tables• Screening Tests• Agreement• Am I forgetting anything?

Page 5: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Bias

• “any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on the risk of a disease.” – Schlessman, 1982

EXPOSURE -> OUTCOME

Page 6: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Why Do We Care About Bias?

• Bias can mask an association between two variables that really are related

• Bias can create a false (spurious) relationship between two variables

• Bias can cause us to overestimate the size of a real relationship

• Bias can cause us to underestimate the size of a real relationship

Page 7: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Selection Bias

• Sometimes called “selection effect”• Error due to a systematic difference between

those who are selected for a study and those who are not

Page 8: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Selection Bias

Page 9: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Total population

Sampled population

Eligible subjects

Subjects asked to participate

Participants

Those who complete study

Lost to follow-up

Non-participants

Exclusions

(sampling scheme)

(inclusion criteria)

(informed consent)

Flow of Subjects Through a StudyWhere does selection bias manifest?

Page 10: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Example

• Study on the relationship between SES and health

• Recruit subjects by sending out flyer for interested participants to show up at 11:AM– Who will show up?

Page 11: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Example

• A study on antibiotic completion rates among different ethnicities in central Europe, including Roma

• Study conducted over weeks from central immobile location

• Roma (nomadic) more likely to be lost to follow-up

Page 12: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Total population

Sampled population

Eligible subjects

Subjects asked to participate

Participants

Those who complete study

Lost to follow-up

Non-participants

Exclusions

(sampling scheme)

(inclusion criteria)

(informed consent)

Flow of Subjects Through a Study

Page 13: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Berkson’s Bias

• A stamp collector has 1000 stamps.• 300 are pretty and 100 are rare• 30 are both pretty and rare• What percentage of all stamps are rare?• What percentage of the pretty stamps are rare?• So does prettiness tell us anything about rarity?

10%

10%

NO

But what if the collector puts 50 stamps on display, and among them are the 30 that are both pretty and rare? Then at least 60% of the displayed ones are both pretty and rare. What does someone viewing the display conclude?

That there is indeed a relationship between prettiness and rarity

Page 14: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Berkson’s Bias

• How does this manifest in epidemiology?• Patients with two diseases are more likely

than patients with one disease to be in hospital

• Therefore if you select your subjects from a hospitalized population, you are more likely to find a spurious relationship between two unrelated diseases

Page 15: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

(Spurious)

Page 16: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Berkson’s Bias

• A type of selection bias• Also called “Berkson’s paradox” or “Berkson’s fallacy”• Named for 1946 paper by Berkson (Berkson J. Limitations of the

application of fourfold table analysis to hospital data. Biometrics 1946;2:47-53)

• “The set of selective factors that lead hospital cases and controls in a case-control study to be systematically different from one another. This occurs when the combination of exposure and disease under study increases the risk of hospital admission, thus leading to a higher exposure rate among the hospital cases than the hospital controls”

Page 17: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Response Bias

• Type of selection bias• those who agree to be in a study may be in

some way different from those who refuse to participate

Ever wonder why people volunteer for studies?

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Information Bias

• A systematic error in measurement• Differential vs nondifferential bias• Recall and interviewer bias

• In other words, the means of obtaining information about your subjects are inadequate or incorrect

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Example

• In a cohort study, babies of women who bottle feed and women who breast feed are compared, and it is found that the incidence of gastroenteritis, as recorded in medical records, is lower in the babies who are breast-fed.

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?

• Lack of good information on feeding history results in some breast-feeding mothers being randomly classified as bottle-feeding, and vice-versa

Page 21: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

(Aside)

• What if the mothers of breast-fed babies are of higher social class, and the babies thus have better hygiene, less crowding and perhaps other factors that protect against gastroenteritis. Crowding and hygiene are truly protective against gastroenteritis, but we mistakenly attribute their effects to breast feeding. Is this bias?

CONFOUNDING

Page 22: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

EXPOSURE(breast/bottle feeding)

OUTCOME(gastroenteritis)

SES(confounder)

Useful guide (but not a rule) for distinguishing between bias and confounding:In confounding, the observation is correct, but the explanation is wrong.In bias, the observation and conclusion are both wrong.

Page 23: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Information Bias

• Misclassification bias is a type of information bias– Eg, some people who have the disease are

labelled as not having the disease, or vice versa– Eg, a population study attempting to compute

prevalence of menopause suffers from misclassification bias because some cases use age-based definition and some use menses-based definition (Int J Epidemiol. 1992 Apr;21(2):222-8.)

Page 24: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Misclassification Bias

• Differential– The rate of misclassification differs in different

study groups– Eg, a study attempts to measure whether mothers

of malformed babies had more infections during pregnancy than did mothers of normal babies

• But women with malformed babies tended to have problematic pregnancies requiring more doctor contact, so were more likely to remember infections, so they were different than those without malformed babies

Page 25: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

• Differential misclassification bias – Errors in measurement are one way only

– Example: instrumentation may be inaccurate, such as using only one size blood pressure cuff to take measurements on both adults and children

• If comparing adults and children, you will consistently get lower readings for the children

Page 26: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Misclassification bias

• Nondifferential– The bias is inherent in the data collection

methodology and does not differ between study groups

– Eg, in a study measuring the relationship between blood pressure and protein intake, the BP cuff was broken for everyone and was in fact giving random results.

– Tends to bias results towards the null hypothesis (dilute study findings)

Page 27: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Information Bias

• Recall Bias– In 1995, the O.J. Simpson trial happened– In 2005, you do a random survey asking people if

they thought he was actually guilty and whether they thought the trial was fair

– Perhaps those who think he’s innocent were more likely to remember the details than those who think he’s guilty

Page 28: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Information Bias

• Recall Bias– In other words, the response to the survey

question is influenced by the respondent’s memory as well as by his actual opinion

Page 29: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Related to Recall Bias

• Response Bias

• Reporting Bias

Page 30: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Related to Recall Bias

• Response Bias– The tendency to answer questions the way you

think the interviewer wants you to answer them– E.g., “Prior to the Haiti earthquake, did you know

the name of Haiti’s capital city?”

• Reporting Bias

Page 31: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Related to Recall Bias

• Response Bias– The tendency to answer questions the way you

think the interviewer wants you to answer them– E.g., “Prior to the Haiti earthquake, did you know

the name of Haiti’s capital city?”

• Reporting Bias– Also called “publication bias”– Tendency to only publish those results that show

a positive result

Page 32: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Interviewer Bias

• Partner of “response bias”– Through tone of voice, body language, etc, an

interviewer and lead a respondent into giving a certain response

– Hence the need for well trained interviewers

Page 33: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Detection Bias

New AIDS Cases Per Year Per 100,000 Population

0

5

10

15

20

25

30

35

90 91 92 93 94 95 96 2000

Latin America

North America

Caribbean

Page 34: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Healthy Worker Bias

• In many countries (usually the West), those who work are a healthy subset of the total population– This is called the Healthy Worker Effect– Therefore studies done on a sample of working

people are problematically generalizable to the whole population

– Eg, blood donors are self-selected on the basis of better lifestyles

Page 35: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Healthy Worker Bias

• Usually important in mortality studies– When comparing mortality rates of a given

profession to the national average (to measure danger of a job) , remember that workers are on average healthier than the norm

Page 36: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Something new?

• “Wish bias”– Tendency for people with a disease to show that

they were not responsible for their disease• Lung cancer patients over-reporting smoking rates

Page 37: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Bias You Have Come Across

• Lots of bias in your abstracts

• Foreign language exclusion bias• Rhetoric bias• Ease of access• One-sided reference bias

Page 38: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Crazy Amounts of Bias

• If you’re interested, a more thorough list is here:– http://www.dorak.info/epi/bc.html

Page 39: HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals453075 Pr(a)

Bias

• Remember: bias is the result of something systematically wrong with the way the study has been designed or implemented

• Bias is therefore entirely or mostly avoidable

See you on the 22nd!