Week 8: Confounding and Effect Modification Flashcards
What is the main purpose of RCTs?
To test the efficacy of an intervention by ensuring similar distributions of known and unknown characteristics between treatment groups
Why are observational studies sometimes used instead of RCTs?
RCTs can be expensive, time-consuming, and unethical for harmful interventions. Observational studies provide an alternative method to analyse exposures and outcomes
Define confounding in the context of statistical analysis
Confounding occurs when a third variable influences both the exposure and the outcome, creating a spurious association
What are the criteria for a variable to be a confounder?
- It is a risk factor for the outcome
- It is associated with the exposure
- It is not a result of the exposure
What is stratified analysis, and why is it used?
Stratified analysis divides data into strata based on potential confounders to control for their effects and analyse the exposure-outcome relationship within each stratum
What is the Mantel-Haenszel Odds Ratio?
A weighted average of ORs across strata, giving more weight to larger strata, used to control for confounding in categorical data
How does logistic regression help in addressing confounding?
Logistic regression models the relationship between exposure and outcome while adjusting for confounding variables
Describe the difference between effect modification and confounding
Effect modification occurs when a third variable alters the strength or direction of the association between the exposure and outcome, whereas confounding creates a spurious association
What methods can address confounding in study design?
Restriction (e.g., limiting participants to a specific group) and matching (e.g., pairing cases and controls on confounding variables)
When is the assumption of homogeneity for Mantel-Haenszel analysis valid?
When the association between exposure and outcome is valid across strata
How is effect modification identified?
By stratifying data on a third variable and observing different exposure-outcome associations across strata
Why should factors on the causal pathway not be adjusted for in analysis?
Adjusting for these factors can bias the estimate of the exposure-outcome relationship
What is the key advantage of randomisation in RCTs?
It minimises confounding by equally distributing known and unknown characteristics between groups
How does matching help in study design?
It pairs subjects with similar values of confounding variables, ensuring balanced groups for analysis
What does a significant test of homogeneity in Mantel-Haenszel analysis suggest?
That there is effect modification, and separate ORs should be reported for each stratum - we wouldn’t use MH
Why is continuous outcome analysis different, and which method is used?
Continuous outcomes require linear regression to assess associations and adjust for confounding
What is the impact of crude analysis on confounding?
Crude analysis may overestimate or underestimate the true association by ignoring confounding variables
Explain the term “interaction” in statistical analysis
Interaction (effect modification) occurs when the relationship between two variables changes across levels of a third variable
How do you calculate Q and R as part of the Mantel-Haenszel OR?
Q (weighted numerator)
= ad/n stratum 1 + ad/n stratum 2
R (weighted denominator)
= bc/n stratum 2 + bc/n stratum 2
How do you calculate Mantel-Haenszel OR?
MH_OR = Q/R
What is the difference between pair and frequency matching?
Pair matching - pair one individual to another individual
Frequency matching - match groups of individuals
What is the H0 for a test of homogeneity (M-H) between smokers and non-smokers?
H0: No difference between OR for smokers and non-smokers
If p > 0.05, we should use the M-H combined estimate - the M-H is a valid overall estimate of the association between exposure and outcome in such cases
How else is effect modification called?
Interaction, heterogeneity between strata
