Table 1.

Summary of known functions and procedures to analyze GRTs using methods described in the current review.

	Software
Method	SAS	Stata	R
Outcomes analysis of all available data
Mixed-effects models	PROC MIXED	mixed	lme4
	PROC NLMIXED	melogit	nlme
	PROC GLIMMIX	mepoisson
Generalized estimating equations (GEE)	PROC GENMOD1	xtgee	geeglm/geeM
Targeted maximum likelihood (tMLE)	N/A	N/A	N/A2
Quadratic inference function (QIF)	%qif	N/A	qif3
Permutation tests	%ptest	N/A	N/A
Accounting for missing outcomes
Multiple imputation for clustered data	%mmi_impute4	REALCOM Impute	pan
	%mmi_analyze	mi impute4	jomo5
Inverse probability weighting (IPW)	PROC GENMOD6	N/A7	CRTgeeDR
Causal-inference based methods8
Augmented GEE (AU-GEE)	N/A	N/A	CRTgeeDR
Doubly robust AU-GEE	N/A	N/A	CRTgeeDR

Footnotes:

¹. PROC GEE is another option, but is in experimental phase and has limited usefulness for GRTs over and above PROC GENMOD.

². In R, tmle is available for tMLE, but at the time of writing, does not allow for clustering.

³. As of the writing, the authors have been unable to load the package and it only allows equal cluster size, but Westgate has modified the code for GRTs with variable cluster size in the appendix of his paper⁶³

⁴. Only useful for continuous outcomes.

⁵. In R, mice is available for multiple imputation but at the time of writing, does not account for clustering.

⁶. Cannot account for imprecision in the weights.

⁷. xtgee cannot accommodate individual-level weights but only group-specific weights.

⁸. Both of the listed methods are related: AU-GEE accounts for baseline covariate imbalance and doubly robust AU-GEE, an extension of AU-GEE, accounts for both baseline covariate imbalance and missing data.

N/A: not available at the time of writing.