Bias and Confounding Flashcards
How can we get the wrong answer?
Systematic error (bias)
Random error (chance)
Systematic Erros (Bias)
A systematic error in selection of the study sample or measurement that can lead to incorrect results.
Selection Bias
Can occur when Study Sample is systematically different from Accessible Population or Target Population
Choosing who to recruit and who is analyzed for the study
Measurement Bias
A systematic error in the measurement of relevant study measures (e.g., exposure, outcome).
Confounding
The true relationship between an exposure and an outcome is obscured by another variable that is associated with both the exposure and the outcome.
Oversampling outcome
There are more subjects with the outcome than would have been expected had the sample been drawn randomly
Oversampling Exposure
There are more subjects with the exposure
than would have been expected had the sample been drawn randomly
Selection Bias can bias
- measures of occurrence (prevalence, incidence)
- measures of association (OR, RR)
- Is most problematic when there is selection by both exposure and outcome
Differential Loss to Follow-up Can Cause Selection Bias
Example
When patients are not showing up for the follow up of their study results so you are unable to see the true real answer.
How do you limit selection bias?
Study Design
- Select a representative sample
- Do not select by both exposure and outcome
- Randomize
Study Procedures
- Recruit subjects to achieve intended sample
- Complete follow-up in those subjects
Measurement bias can occur when
Actual Measurements are systematically different from Intended Variables or Target Phenomenon
Misclassification of Outcome
Subjects who should have been classified
as having the outcome are misclassified as
not having the outcome
Misclassification of Exposure
Subjects who should have been classified
as having the exposure are misclassified
as not having the exposure.
Differential Misclassification
Misclassification occurs with different probabilities by outcome or exposure
Measurement Bias can bias
- measures of occurrence
- measures of association
- Is most problematic when it is differential by both exposure and outcome
how to limit measurement bias
Study design:
Use most sensitive and specific measurements available
- “Gold-standard” measurements (closest representation to the truth phenomena)
- Validated self-report measures
- Consider duplicate measurements to reduce variability
Same measurements of exposure and outcome in all subjects
- Randomization and Blinding
Study procedures:
Operations manuals/training for standard measurement
Is confounding a bias?
No, it’s a phenomenon that occurs due to
associations between variables and requires careful consideration.
Three criteria for confounding
- Variable must be associated with the exposure (cause it)
- Variable must be associated with the outcome (cause it)
- Variable cannot be on the causal path between the exposure and the outcome
How to Address Confounding
Study Design
- Restriction (breast cancer: study women more than men; age: things happen differently in kids vs adults)
- Matching
- Randomization
Study Analysis
- Stratification
- Regression
Restriction
- Include only participants with some value of the confounder
- Example: Age is a confounder in association between Smoking and Death, so, exclude people under 65 in your study
- Issues
1. Irrevocable (you can’t go back and change it)
2. Limits external validity/generalizability
Matching
- Equal numbers of participants with an important confounder
- Often employed in case-control studies
- Must be accounted for in design, study procedures, and analysis
- Issues: Irrevocable, Sometimes not feasible
Stratification
- Divide the study population into groups based on the confounder
- Estimate the measures of occurrence/association separately in the stratified groups (Can use methods to create combined measures)
Randomization
- If participants are randomly assigned the exposure, then the
exposure is not associated with anything!
1. Limits selection bias
2. Measurement error will not affect ratio measures - Issues:
1. Differential loss to follow-up
2. Generalizability/external validity issues depending on inclusion/exclusion criteria and Study Sample
Regression
it allows you to look at variables in all different ways
What should you adjust for?
Factors known or suspected to be associated with exposure and outcome
* Find in
literature
* Observed in
data
Factors not on causal pathway
Confounding – Critical Review of Literature
- Did they consider things that might be associated with the exposure and outcome?
- Did they include factors on the causal pathway?
- Were methods employed to limit confounding (e.g., restriction, matching, randomization, stratification, regression)?
- Are there important unmeasured (or unmeasurable) confounders that could affect the interpretation of results?
How to Address Confounding
Directed Acyclic Graphs (DAGs)
DAG Rules
-Directed - Arrows can only go one way
-Acyclic - There cannot be a loop where following the arrows
ends back at the variable
Systematic errors (bias) is especially problematic when
related to exposure AND outcome
Directed Acyclic Graphs (DAGs) can visualize relationships between
exposure and outcome, confounders, and mediators
