In this section, we present an expanded set of results from Section 4 of the main paper. Specifically, we provide tables for the exposure effect size, bias, estimated and robust standard errors, reduction in RMSE, statistical power, and statistical coverage. For r = 1 , ..., R trials, we compute the augmented GEE estimates of exposure effect size ̂ β$_{r}$ and estimate standard deviation ̂ sd$_{r}$ ($_{̂}$ β$_{r}$ ) . After calculating β as detailed in the paper, we obtain the estimated empirical bias and empirical variance of the exposure effect estimate, where To combine the amount of bias and size of the standard error in a single composite measure, we compute the root mean squared error for each trial, defined as: Our metric for the gained improvement by the augmentation term is the percent reduction in RMSE for each adjustment covariate set, comparing the augmentation adjustment ̂ RMSE$_{adj}$ to that of the unadjusted GEE ( ̂ RMSE$_{GEE}$: Statistical coverage is the probability that the estimated confidence intervals cover the true exposure effect, which must be 95% or greater for valid inference: Finally, in our setting with the null hypothesis β = 0 , power is the probability that the confidence interval is significantly different from zero given that the exposure effect is not zero: The average values for these estimands are given in the main paper. T 29 ̂ Power := P (0 / ∈ CI ( ˆ β ∗ $_{A}$) | β$_{A}$ = 0) (10) ̂ Coverage := P ( β ∗ A ∈ CI ( ˆ β $^{∗}$)) . (9) ̂ Gain := 100 ×$^{(}$ 1 − ̂ RMSE$_{adj}$ ̂ RMSE$_{GEE}$$^{)}$ . (8) ̂ RMSE := √ 1 R ∑ r ̂ Bias$_{r}$ ($_{̂}$ β$_{r}$ ) 2 + ̂ Var ($_{̂}$ β ) . (7) ̂ Bias ($_{̂}$ β$_{r}$ ) = β −$_{̂}$ β$_{r}$. (6) 4 Extended Simulation Results