Table 1.

Effect estimates from simulated data sets using the causal structure of the aspirin example.

Mediator measured at:	Time 2.5	Time 5
Setup 1 (aspirin example)
Mediator ($*{10}^{-3}$)	0.311 (0.047)	0.411 (0.46)
Exposure ($*{10}^{-3}$)	−352 (11)	−328 (11)
Total effect ($*{10}^{-3}$)	−426 (1.4)	−426 (1.4)
Mediated proportion	17%	23%
Setup 2
Mediator ($*{10}^{-3}$)	1.15 (0.034)	1.11 (0.028)
Exposure ($*{10}^{-3}$)	−126 (6.1)	−75.2 (6.5)
Total effect ($*{10}^{-3}$)	−289 (5.3)	−289 (5.3)
Mediated proportion	56%	74%
Setup 3
Mediator ($*{10}^{-3}$)	1.38 (0.032)	1.26 (0.027)
Exposure ($*{10}^{-3}$)	−76.2 (3.8)	−14.9 (4.6)
Total effect ($*{10}^{-3}$)	−190 (4.7)	−190 (4.7)
Mediated proportion	60%	92%

In setup 1, the parameter values are taken from the aspirin example while setup 2 and 3 have progressively slower speed of treatment effect on mediator. However, the total effect of treatment once treatment has taken full effect is the same in all setups. The table reports estimates from ordinary regression with standard errors in parentheses. The first two rows of each setup are from a joint regression of mediator and treatment on outcome, the third row is the total effect of treatment on outcome, and the fourth row presents the mediated proportion (i.e. how large a proportion of the total effect is mediated through the mediated). In the left column the mediator is measured at time point 2.5 (i.e. midway in the observation window) while in the right column the mediator is measured simultaneously with the outcome (i.e. at time point 5).