Figure 1.

Causal diagrams depicting relationships among key variables, illustrating the issue of time-dependent confounding.

a: Actual relationships in population, where the intermediate variable L is a time-dependent confounder.

b: Relationships in the pseudo-population created by weighting (condition 2 is removed)

A₀: Initial treatment

A₁: Subsequent treatment

L: Intermediate variable (disease activity)

Y: Outcome (visual acuity)

U: Common cause (possibly unmeasured) of L and Y

Conditions 1 Covariate L is independent predictor of outcome Y because of 1a (effect of L on Y) and/or 1b (unmeasured common cause U of L and Y)

2 Covariate L influences subsequent treatment A₁

3 Covariate L is influenced by earlier treatment A₀.