Table 2.

Data example of a replicate/scenario, estimated effects (coefficients from logistic models) of exposure (X ₁), and known “causal”/confounding factors of B on B and proxy outcome C.

	Effects on B		Effects on C		Indicators for real causal factors   (1 = Yes, 0 = No)
	Coefficient	P value	Coefficient	P value	Causal to B	Causal to C
X 1	0.12	0.001	0.16	0.000	0	0
X 2	∗		∗		1	1
X 3	∗		∗		1	0
X 4	0.39	0.000	0.62	0.000	0	0
X 5	∗		∗		0	1
X 6	∗		∗		0	1
X 7	0.24	0.000	0.43	0.000	0	0
X 8	∗		∗		1	1
X 9	∗		∗		0	1
X 10	∗		∗		0	0
X 11	∗		∗		0	0
X 12	1.22	0.000	0.88	0.000	1	1
X 13	1.15	0.000	0.17	0.000	1	0
X 14	0.23	0.000	0.27	0.000	0	0
X 15	0.16	0.000	1.04	0.000	0	1
X 16	0.31	0.000	1.56	0.000	0	1
X 17	0.13	0.000	0.19	0.000	0	0
X 18	∗		∗		1	1
X 19	∗		∗		0	1
X 20	0.28	0.000	0.43	0.000	0	0
X 21	∗		∗		0	0
X 22	0.17	0.000	0.24	0.000	0	0
X 23	∗		∗		0	0
X 24	∗		∗		0	0
X 25	∗		∗		0	0
X 26	∗		∗		0	0
X 27	∗		∗		0	0
X 28	0.23	0.000	0.26	0.000	0	0
X 29	0.31	0.000	0.58	0.000	0	0
X 30	∗		∗		0	0
X 31	∗		∗		0	0
X 32	∗		∗		0	0
X 33	∗		∗		0	0
X 34	∗		∗		0	0
X 35	∗		∗		0	0
X 36	∗		∗		0	0
X 37	∗		∗		0	0
X 38	∗		∗		0	0
X 39	∗		∗		0	0
X 40	∗		∗		0	0

*indicates variable is not known as a “causal” factor for B, therefore is not included in the models.