Error & Bias in Epidemiological Studies Flashcards
How are the 2 types of error related to precision and accuracy?
- RANDOM ERROR due to chance has low precision but is accurate
- SYSTEMATIC ERROR not due to chance has low accuracy but is consistent (has a bias)
What is accuracy? Bias?
ACCURACY = whether there is agreement between a measurement made on an object and its true value
BIAS = difference between the average measurements made on the same object and its true value (not accurate = bias)
What are the 3 major types of bias?
- SELECTION BIAS - related to procedures used to selec units for the study —> study groups differed from source population
- INFORMATION BIAS - misclassification related to the information recorded for the study where units are incorrectly assigned positive/negative exposure or disease
- CONFOUNDING BIAS - some other factor changes or distorts the effect of exposure on the outcome (diseased vs. non-diseased)
What are the consequences of bias like in descriptive and explanatory studies?
DESCRIPTIVE = outcome only affected —> higher or lower estimates of disease frequency
EXPLANATORY = outcome and explanatory variables taken into account —> altered disease frequency moves effect estimate towards or away from the null (no statistical difference)
What does it mean to move toward or away from the null? Which is better?
TOWARD = bias causes the study to observe no real effect of exposure/risk factor on disease status
AWAY = bias causes the study to observe that exposure/risk factors have more effect on disease status than they actually do
TOWARD NULL - better to underestimate and do further studies
How is magnitude of bias calculated?
difficult —> sensitivity simulations can help
What is surveillance bias?
selection bias where mild/subclinical disease is more likely to be detected in animals under frequent medical surveillance and/or enrolled in surveillance programs
What is referral bias (admission risk bias/Berkson’s fallacy)?
selection bias where differential referral patterns are a source of bias in hospital-based case-control studies
- is the hospital population representative of the whole population?
- socioeconomic representation in hospital population
What is non-response bias?
selection bias where >20-30% of non-responses or refusal to participate in a study may contribute a bias
- only accounts for passionate people in the study
- high overall response = less bias
What is missing data bias?
selection bias where >20-30% of data is missing and an accurate result cannot be attained
- not enough serum from blood draw to run a test
- missing data does not confirm non-diseased state
What is loss to follow-up bias?
participants are dropping out of a study, which can alter the new group, making them less representative of the actual population
What is selective sentry (survival) bias?
traits are naturally selected when choosing a group of subjects and treatments that prolong lifespan increase prevalence of disease
- “healthy worker” effect in occupational health studies - those working tend to be more healthy than unemployed
In what 4 ways can selection bias be reduced? How can it NOT be corrected?
- random sampling - assesses probability of bias by distributing risk factors equally between groups
- maximize response rates - questionnaires are enticing to get more participants to respond
- minimize withdrawal rates - keep participants in study
- ensure equal responses/withdrawals from exposed/non-exposed and diseased/non-diseased
analytical techniques
How can selection bias be reduced in observational studies?
consider the forces at play with selecting individuals
- case-control: use incidental cases and get controls from the same source population as the cases
- cohort: persistent follow-up with creative strategies for maintaining full participation
How can selection bias be reduced in controlled trials?
RANDOMIZE allocation to intervention and comparison groups and BLIND recruiters and participants to allocation (exposed vs non-exposed), while minimizing withdrawals and maximizing retention
(randomize and blind…everything should be fine)
What is recall bias?
information bias where cases are better at recalling past exposure compared with non-cases
- Salmonella + cases are likely to remember what they last ate compared to Salmonella - cases
What is interview bias?
information bias where interviewers are privy to the hypothesis under investigation
- more likely to ask leading questions to support hypothesis
What is obsequiousness bias (Clever Hans effect)?
information bias where subjects systematically alter responses toward perceived desirable answers
- people commonly change answers based on welfare and hygiene/sanitation based on what should be
- Hans was a trained horse who could supposedly perform arithmetic, but it was found tat he was getting non-verbal cues from his trainer on when to stop stomping his hoof
What is the non-differential consequence of information bias (misclassification) like? How does it compare to null?
systematic errors in one group are independent of the other group where there are equal amounts of systemic error in E regardless of D status and systemic error in D regardless of E —> best possible bias
errs toward null —> decreased power and ability to find an effect
What is the differential consequence of information bias (misclassification) like? How does it compare to null?
systematic error occurs to a greater extent in one group than another —> unequal amount of systemic error in E and D DEPENDING on D and E status —> worst bias, throws off RR and OR
err in any direction (toward or away from null) —> unbalanced link to disease status
What are 4 ways to reduce information bias (misclassification)?
- E and D status should be assessed independently - should be blind to the status of cohorts, cases, and controls
- use rigorous and valid methods for determining D and E - explicit case definitions, best available test + confirmatory test, measure specific exposures (not general)
- use complete and detailed sources of information - complete exposure histories with as much info as possible
- use objective measures when available - no leading questions, clear cut answers
How can interviews and questionnaires be used to reduce information bias (misclassification)?
- minimize time between diagnosis and questioning
- use validated survey instruments (pilot study to test question clarity and detail level)
- standardized interview protocols with clear guidelines
- well-trained qualified interviewers vs. mail/phone
- state/demonstrate clear confidentiality of information
How can information bias (misclassification) be corrected after the study? Why should this be done carefully? What way cannot be used?
validation study where a sub-sample from the study is used to verify classification of E and D and post-hoc adjustments
very sensitive to changes in estimates —> much better to prevent information bias than to correct it
analytical techniques
How can information bias (misclassification) be reduced in observational studies?
CASE-CONTROL: explicit definitions for cases, determining E status independent from D status, interview as soon as possible
COHORT: determining D status independent from E status, valid method and objective measures for determining D status
