HSS4303B – Intro to Epidemiology Feb 11, 2010

JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals Pr(a) = relative observed agreement = ( )/ 75 = 90.7% Hasselhoff’s Responses Shatner’s Responses

Pr(a) = relative observed agreement = ( )/ 75 = 90.7% Pr(e) = prob that agreement is due to chance = (44x45/ (31x30)/75 2 = = 51.7% (multiply marginals and divide by total squared) JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals Shatner’s Responses 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 Study Where 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% 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 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 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: –

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 22 nd !