Table IV.

Comparison of the Bayesian design of Huang et al. [15], using adaptive randomization (AR) or equal randomization (ER), with its frequentist counterpart.

Case	Frequentist			Bayesian AR			Bayesian ER
	pr(S)	E(T)	E(N)	pr(S)	E(T)	E(N)	pr(S)	E(T)	E(N)
A	0.049	4.97	462	0.221	5.23	446	0.018	6.90	517
B	0.050	4.48	445	0.220	5.37	450	0.015	6.95	518
C	0.050	4.87	456	0.368	5.07	431	0.027	6.84	514
D	0.018	3.88	407	0.900	3.09	318	0.231	6.36	503
F	0.040	4.99	460	0.197	5.54	463	0.021	6.84	514
G	0.033	4.06	424	0.205	5.61	466	0.016	6.95	519
1	0.909	5.45	489	0.995	2.68	286	0.981	4.82	451
2	0.932	5.28	487	0.998	2.51	273	0.988	4.55	440
3	0.938	5.10	482	1.000	2.39	257	0.958	4.54	439
4	0.927	5.02	481	0.997	2.27	242	0.857	4.52	441
5	0.918	5.09	487	1.000	2.34	254	1.000	3.91	418
6	0.934	5.10	481	0.999	2.32	251	0.999	3.93	421
7	0.914	5.35	487	0.998	2.69	298	1.000	4.07	434
8	0.908	5.41	488	0.990	3.00	321	0.982	4.89	456
9	0.912	5.43	487	0.995	2.85	306	0.986	4.82	453

The baseline survival distribution is Weibull with hazard function λ₀(t) = 0.45t^0.8.