TABLE 2.

Datasets generated and simulations results for cluster randomized trials (CRTs) with missing outcome data based on a single fully observed baseline binary covariate

(a) Datasets generated
		$Y_i$ continuous, $g$ identity		$Y_i$ binary, $g$ identity		$Y_i$ binary, $g$ logit
Scenario		1	2	1	2	1	2
Parameters	${\mu }_{11}$	0.9	0.62	0.9	0.62	0.9	0.62
	${\mu }_{21}$	0.3	0.95	0.3	0.95	0.3	0.95
	${\mu }_{10}$	0.15	0.63	0.15	0.63	0.15	0.63
	${\mu }_{20}$	0.85	0.74	0.85	0.74	0.85	0.74
	${\sigma }_{11}^2$	0.026	0.41955	0.09	0.2356	0.09	0.2356
	${\sigma }_{21}^2$	0.294	0.026	0.21	0.0475	0.21	0.0475
	${\sigma }_{10}^2$	0.026	0.46795	0.1275	0.2331	0.1275	0.2331
	${\sigma }_{20}^2$	0.229	0.026	0.1275	0.1924	0.1275	0.1924
	$g\left({\mu }_1\right)-g\left({\mu }_0\right)$	0.1	0.1	0.1	0.1	0.41	0.52
Sample size	$n_{\text{C-standard}}$	1524	1524	1494	1172	1514	1200
	$n_{\text{C-IPRW}}$	1360	1622	1370	1200	1388	1232
	$n_{\text{C-known}}$	1476	1640	1486	1216	1506	1254
	$n_{\text{C-approx}}$	1538	1538	1506	1182	1528	1210
	$K_{\text{C-standard}}$	306	306	320	236	304	240
	$K_{\text{C-IPRW}}$	272	326	274	240	278	248
	$K_{\text{C-known}}$	296	328	298	244	302	252
	$K_{\text{C-approx}}$	308	308	302	238	306	242
For all scenarios: $\kappa =0.5,{\pi }_1={\pi }_2=0.5,\delta =0.05,m=5$, $$\begin{gathered} \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{i}\mathrm{t}\left({\beta }_{11}\right)=P\left(R_{ki}=1|X_{ki}=1,Z_k=1\right)=0.7,\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{i}\mathrm{t}\left({\beta }_{21}\right)=P\left(R_{ki}=1|X_{ki}=2,Z_k=1\right)=0.9, \\ \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{i}\mathrm{t}\left({\rho }_{10}\right)=P\left(R_{ki}=1|X_{ki}=1,Z_k=0\right)=0.75,\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{i}\mathrm{t}\left({\rho }_{20}\right)=P\left(R_{ki}=1|X_{ki}=2,Z_k=0\right)=0.85 \end{gathered}$$
(b) Simulation results
		$Y_i$ continuous, $g$ identity		$Y_i$ binary, $g$ identity		$Y_i$ binary, $g$ logit
Scenario		1	2	1	2	1	2
Sample size	$n_{\text{C-standard}}$	1524	1524	1494	1172	1514	1200
IPRW estimator	power $\left(\mathrm{\%}\right)\left[g\left({\hat{\mu }}_1\right)-g\left({\hat{\mu }}_0\right)\right]$	93 [0.10]	89 [0.10]	94 [0.10]	91 [0.10]	94 [0.41]	91 [0.52]
Sample size	$n_{\text{C-IPRW}}$	1360	1622	1370	1200	1388	1232
IPRW estimator	power $\left(\mathrm{\%}\right)\left[g\left({\hat{\mu }}_1\right)-g\left({\hat{\mu }}_0\right)\right]$	90 [0.10]	90 [0.10]	91 [0.10]	90 [0.10]	92 [0.41]	91 [0.52]
Sample size	$n_{\text{C-known}}$	1476	1640	1486	1216	1506	1254
IPRW estimator	power $\left(\mathrm{\%}\right)\left[g\left({\hat{\mu }}_1\right)-g\left({\hat{\mu }}_0\right)\right]$	92 [0.10]	90 [0.10]	94 [0.10]	91 [0.10]	94 [0.41]	91 [0.52]
Sample size	$n_{\text{C-approx}}$	1538	1538	1506	1182	1528	1210
IPRW estimator	power $\left(\mathrm{\%}\right)\left[g\left({\hat{\mu }}_1\right)-g\left({\hat{\mu }}_0\right)\right]$	93 [0.10]	88 [0.10]	94 [0.10]	90 [0.10]	94 [0.40]	91 [0.52]

$g\left({\hat{\mu }}_1\right)-g\left({\hat{\mu }}_0\right)$ displays the average estimated intervention effect across the 10 000 simulations