Testing hypotheses under covariate-adaptive randomisation and additive models

Figures & data

Table 1. Simulation power in % under linear model (, N=200, 10,000 simulation runs when , 2000 simulation runs when ).

	SR		CABC
0	4.97	4.96	1.91	3.06	5.37	5.49	5.35
0.1	8.40	10.15	5.00	7.40	11.50	11.40	11.35
0.2	19.82	24.62	16.82	22.12	27.83	28.38	28.88
0.3	40.90	48.00	37.65	46.55	53.85	54.70	54.75
0.4	62.85	70.70	64.00	72.40	78.05	78.55	78.40
0.5	81.30	88.35	85.10	90.50	93.20	93.25	93.55
0.6	92.95	97.10	96.50	97.85	98.65	98.80	98.90
0.7	98.05	99.25	99.30	99.75	99.80	99.85	99.75
0.8	99.55	99.90	99.85	99.85	99.90	99.90	99.95
0.9	99.90	100.00	100.00	100.00	100.00	100.00	100.00

Notes: : two sample t-test; : bootstrap t-test; : Wald's test, in this case, one-way analysis of covariance test; : calibrated t-test, with variance estimated by ; : calibrated Wald's test, with variance estimated by ; SR: simple randomisation; CABC: covariate-adaptive biased coin.

Table 2. Simulation power in % under logistic model (, N=200, 10,000 simulation runs when , 2000 simulation runs when ).

	SR		CABC
0.0	5.25	5.01	1.13	5.03	5.24	5.75
0.1	5.57	6.00	1.44	5.82	5.79	6.41
0.2	6.84	8.44	2.27	8.05	8.42	8.98
0.3	9.13	12.34	4.21	12.08	12.15	12.90
0.4	12.72	17.53	6.93	17.91	17.84	18.67
0.5	17.82	25.35	10.39	24.90	23.54	25.05
0.6	20.80	33.30	18.05	35.15	33.25	34.35
0.7	27.51	43.22	25.10	44.68	42.17	43.32
0.8	35.34	53.16	33.33	55.27	52.76	54.27
0.9	43.30	63.20	44.35	66.30	64.25	65.80
1.0	52.05	73.95	55.50	75.40	73.70	74.60
1.1	60.59	81.88	66.01	82.43	80.87	81.93
1.2	69.80	87.75	74.90	88.30	86.90	87.50
1.3	77.36	92.87	82.83	92.72	91.37	91.92
1.4	83.05	95.55	88.10	95.40	94.35	94.80
1.5	88.55	97.29	91.67	97.34	96.59	96.84

Notes: : two sample t-test; : bootstrap t-test; : Wald's test; : calibrated t-test, with variance estimated by ; SR: simple randomisation; CABC: covariate-adaptive biased coin.