L3: Bias and confounding part 2 Flashcards
recall bias?
Recall bias = differential misclassification of exposure
Recall bias can occur in case-control studies when cases recall their past exposures differently than controls
Amount of time lapsed between the exposure and the recall is an important indicator of the accuracy of recall – therefore, cases and controls should have same recall period
non differential misclassification?
Nondifferenttial misclassification
Less problematic.
Classification error that does NOT depend on values of other variables, i.e., Exposure
nondiff. misclassification: the proportion of subjects misclassified on exposure does not depend on disease status
Disease nondiff. misclassification: the proportion of subjects misclassified on disease does not depend on exposure
Lab error is an important example of non-differential misclassification
confounding?
Confounding may be considered a confusion of effects
The apparent effect of the exposure of interest is distorted because the effect of an extraneous factor (confounder) is mistaken for or mixed with the actual exposure effect (which may be null), leading to bias.
Criteria for a confounding factor
Associated with the outcome of interest independent of its relation to the exposure of interest
Associated with the exposure of interest in the study base that produced the cases
Cannot be an intermediate variable on the causal chain leading from the exposure of interest to the onset of disease
Copy and paste examples of confounders
Should be able to talk about confoundera: study adjusted for age? Age is a confounder? Example.
control of confounding?
Randomization:
How it controls confounders: By randomly assigning participants to different groups, confounders like age,sex, ethnicity are equally distributed between groups. This reduces the likelihood that confounders will affect the study results.
Restriction:
How it controls confounders: By limiting the sample to a specific group (e.g., only people under 30), it removes the influence of certain confounders (e.g., age). This ensures that differences observed are not due to those factors.
Matching:
How it controls confounders: By pairing people with similar confounding factors (e.g., age), the comparison between cases and controls becomes fairer, reducing the effect of those confounders on the outcome.
Stratification:
How it controls confounders: Dividing the population into subgroups (e.g., under 30 vs. 30+) allows you to compare people within those groups. This isolates the effect of the exposure from confounding variables, as you’re looking at each subgroup separately.
Modeling:
How it controls confounders: Statistical techniques like regression adjust for confounders by mathematically accounting for their influence. This helps isolate the true relationship between the exposure and outcome.
Confounder- associated with outcome. Age being the same for controls means it is no longer associated with outcome.
Modelling - not at the level of study design
Use regression analysis to calculate risk ratios. First look at effect of exposure on outcome - univariant analysis? When bringing in other variable - adjusment analysis?
When talking about modelling: when mentioning multivariant regression analysis??
Took these variables into account when looking at risk ratios??
residual confounding?
Nevertheless, many studies report that the association of exposure and outcome persist after adjustment for the measured confounders. The association may still be the result of residual confounding 🡪 as most studies do not measure all potential confounders or do not measure these confounders perfectly or do not determine whether they change across time.
For example, other diseases that may affect both physical activity and mortality, such as diabetes or depression, are often not taken into consideration.
Definition: Residual confounding happens when a study adjusts for a confounder (e.g., BMI, age) but doesn’t fully account for its effect. This could be because:
The adjustment was imperfect (e.g., BMI measured imprecisely, like self-reported vs. actual).
Related factors weren’t measured (e.g., diet quality alongside meat intake).
Categories were too broad (e.g., “smoker vs. non-smoker” misses smoking intensity).
Result: Some of the confounder’s influence lingers, skewing the main factor-outcome link (e.g., sleep-diabetes, meat-cancer).
i.e: Confounding can be altered at the study design stage:
Matching
Restriction
Randomization
confounders. The association may still be the result of residual confounding
when answering an essay question always say residual confounders may remain
effect modification?
The strength of the association between the exposure and outcome varies by levels of a third variable. Effector modifcation has nothing to do with internal validity, it is a biological thing. What it is: Effect modification occurs when the strength or direction of the relationship between an exposure and an outcome varies depending on the level of a third variable (called an effect modifier).
Key point: It’s biological or nature-related, not something you try to control or eliminate in a study design like confounders.
What you do: If you find an effect modifier, you report it because it reflects real, biologically meaningful differences in how the exposure affects different groups.
Nothing to do with study design. Belongs to nature. Have to report it. But confounder- try to minimise in study and has an effect on internal validity. Some variables can be both. Age can be a confounder or an effector modifier. The study might run age adjusted analysis (modelling) or age stratified analysis. (stratification could be used to adress confoundings aswell).
Example of effect modification
The effect of physical activity assessed by questionnaire on the risk for chronic heart disease (CHD) may depend on BMI.
The risk of disease among the least-active people might be greatest among those having the highest BMI.
If so, BMI would be considered an effect modifier in the association between activity and CHD.
confounding and effect modificiation?
Potential for confounding and effect modification increases difficulty of interpreting epidemiologic studies of the direct effects of physical activity on health.
Age can be a confounder because of a direct association between age and increased risk for death and most chronic diseases.
Age can also change the magnitude of risk associated with other variables; hence age can also be an effect modifier.
effect modifiers vs confounding factors?
effect modififer:
- belongs to nature
- different effects in different strata
- simple
- useful
- increases knowledge of biological mechanism
- allows targeting of public health action
confounding factor:
- belongs to study
- adjusted OR/RR different from crude OR/RR
= distortion of effect
- creates confusion in data
- prevent (design)
- control (analysis)
generalisability?
Can results coming from specific pop be generalised?
Is there any reason to believe that the findings would not apply to other populations?
Answers involves a judgment based on an understanding of research hypothesis/mechanism.
