TABLE 1.

Datasets generated and simulations results for individually randomized trials (IRTs) 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	3	4	1	2	1	2
Parameters	${\pi }_1$	0.5	0.5	0.7	0.7	0.5	0.5	0.5	0.5
	${\mu }_{11}$	0.9	0.62	0.2	0.2	0.9	0.62	0.9	0.62
	${\mu }_{21}$	0.3	0.95	0.3	0.3	0.3	0.95	0.3	0.95
	${\mu }_{10}$	0.15	0.63	0.1	0.1	0.15	0.63	0.15	0.63
	${\mu }_{20}$	0.85	0.74	0.2	0.2	0.85	0.74	0.85	0.74
	${\sigma }_{11}^2$	0.026	0.41955	0.01	0.01	0.09	0.2356	0.09	0.2356
	${\sigma }_{21}^2$	0.294	0.026	0.3	0.3	0.21	0.0475	0.21	0.0475
	${\sigma }_{10}^2$	0.026	0.46795	0.01	0.01	0.1275	0.2331	0.1275	0.2331
	${\sigma }_{20}^2$	0.229	0.026	0.3	0.3	0.1275	0.1924	0.1275	0.1924
	$\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{i}\mathrm{t}\left({\beta }_{11}\right)$	0.7	0.7	0.64	1	0.7	0.7	0.7	0.7
	$\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{i}\mathrm{t}\left({\beta }_{21}\right)$	0.9	0.9	1	0.64	0.9	0.9	0.9	0.9
	$\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{i}\mathrm{t}\left({\beta }_{10}\right)$	0.75	0.75	0.64	1	0.75	0.75	0.75	0.75
	$\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{i}\mathrm{t}\left({\beta }_{20}\right)$	0.85	0.85	1	0.64	0.85	0.85	0.85	0.85
	$g\left({\mu }_1\right)-g\left({\mu }_0\right)$	0.1	0.1	0.1	0.1	0.1	0.1	0.41	0.52
Sample size	$n_{\text{standard}}$	1314	1314	558	468	1288	1012	1306	1034
	$n_{\text{IPRW}}$	1150	1412	434	630	1164	1038	1180	1068
	$n_{\text{known}}$	1266	1430	436	634	1280	1056	1298	1088
	$n_{\text{approx}}$	1328	1328	582	488	1300	1020	1318	1044
For all scenarios: $\kappa =0.5$, ${\pi }_2=1-{\pi }_1$
(b) Simulation results
		$Y_i$ continuous, $g$ identity				$Y_i$ binary, $g$ identity		$Y_i$ binary, $g$ logit
Scenario		1	2	3	4	1	2	1	2
Sample size	$n_{\text{standard}}$	1314	1314	558	468	1288	1012	1306	1034
IPRW estimator	power $\left(\mathrm{\%}\right)\left[g\left({\hat{\mu }}_1\right)-g\left({\hat{\mu }}_0\right)\right]$	93 [0.10]	87 [0.10]	96 [0.10]	80 [0.10]	93 [0.10]	89 [0.10]	93 [0.41]	90 [0.52]
Sample size	$n_{\text{IPRW}}$	1150	1412	434	630	1164	1038	1180	1068
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]	90 [0.10]	90 [0.10]	90 [0.10]	89 [0.10]	90 [0.41]	90 [0.52]
Sample size	$n_{\text{known}}$	1266	1430	436	634	1280	1056	1298	1088
IPRW estimator	power $\left(\mathrm{\%}\right)\left[g\left({\hat{\mu }}_1\right)-g\left({\hat{\mu }}_0\right)\right]$	92 [0.10]	91 [0.10]	90 [0.10]	90 [0.10]	93 [0.10]	91 [0.10]	92 [0.41]	91 [0.52]
Sample size	$n_{\text{approx}}$	1328	1328	582	488	1300	1020	1318	1044
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]	96 [0.10]	81 [0.10]	92 [0.10]	89 [0.10]	93 [0.40]	90 [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