Su et all, 2012
Problem
Confounding Variable
Interaction (Effect modification/moderation)
Solution
Design
Stratification
Matching
Cohort Restriction
Randomization
Covariate Imbalance (High Dimension)
Solution: Covariate Adjustment
Over-adjustment
Under-adjustment
Confounder is uncollected in the data or excluded from the model
A mediator is mistakenly considered as a confounder and included in the model for adjustment.
Controlling for a collider that correlates with both the treatment and the outcome via an "M-diagram"
Analysis
ANCOVA: analysis of covariance
DAG: directed acyclic graph
Problem: Different treatment effects at different levels or values of covariates.
Hard to interpret
Change direction/degree of its causal inference on the outcome:
Type
Non - linear Interaction
Quantitative Interaction
1st, 2nd, higher order interaction
Qualitative Interaction
Treatment- by- covariate interaction: Directional change in terms of treatment preference
Subgroup analysis:
Definition
Extract the maximum amount of information
Difficulties (malpractice)
Subgroup determination
Multiple testing
Lack of power
Explore the heterogeneity of treatment effect across sub-population
Extract the maximum amount of information