HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They...
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Transcript of HSS4303B – Intro to Epidemiology Feb 11, 2010. JudgeYes They Are HotNo They Are NotTotals Yes They...
HSS4303B – Intro to Epidemiology
Feb 11, 2010
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
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
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?
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
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
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
Selection Bias
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?
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?
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
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
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
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
(Spurious)
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”
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?
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
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.
?
• Lack of good information on feeding history results in some breast-feeding mothers being randomly classified as bottle-feeding, and vice-versa
(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
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.
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.)
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
• 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
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)
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
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
Related to Recall Bias
• Response Bias
• Reporting Bias
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
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
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
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
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
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
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
Bias You Have Come Across
• Lots of bias in your abstracts
• Foreign language exclusion bias• Rhetoric bias• Ease of access• One-sided reference bias
Crazy Amounts of Bias
• If you’re interested, a more thorough list is here:– http://www.dorak.info/epi/bc.html
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!