Table 1.

Comparison of mean percent bias for QGC and WQSBSPT.

	Mean percent bias (%) (200 samples of $n=500$)
Estimand	QGC	WQSBSPT
Component coefficient, “high” effect size	2	$-5$
Component coefficient, “low” effect size	6	48
Overall effect size	0.1	8

Note: Mean percent bias for two contrasting approaches (QGC without bootstrapping and weighted quantile sum regression using WQSBSPT) to estimate the effects of a mixture using the data simulation methods of Day et al.¹ with a “correlated mixture” developed with the empirical covariance matrix reported in the appendix of their paper. QGC, quantile-based g-computation without bootstrapping; WQSBSPT, weighted quantile sum regression bootstrap sample permutation test.