. 2020 Apr 7;128(4):047004. doi: 10.1289/EHP5838

Table 2.

Single simulation demonstrating equivalence between WQS and quantile g-computation in large samples ( $n = 100,000$ ) when all exposures have effects in the same direction (true $ψ = 5.0$ ; $true weights = 0.5, 0.25, 0.15, and 0.1$ ).

Method	Mixture effect		Estimated weights
Method	$ψ$	t-Statistic	$w_{1}$	$w_{2}$	$w_{3}$	$w_{4}$
WQS^a	5.00	806	0.50	0.25	0.15	0.10
Q-gcomp^b	5.00	884	0.50	0.25	0.15	0.10

^{^a}

Weighted quantile sum regression (R package gWQS version 2.0 defaults).

^{^b}

Quantile g-computation (R package qgcomp version 1.3 defaults).