. Author manuscript; available in PMC: 2020 Jan 14.

Published in final edited form as: J Biopharm Stat. 2019 Apr 24;30(1):69–88. doi: 10.1080/10543406.2019.1607368

Table 4.

Impact of time-trend on operating characteristics of BRAR with early stopping rule maximum sample size=302, burn-in size=50, allocation update frequency=28, Efficacy stop=0.99, # simulation =1000.

Algorithms	Time-trend effect	Efficacy profile	BRAR(1/2,σ²), time trend
Algorithms	Time-trend effect	Efficacy profile	Sample size	% better arm (SD)	Type I error/Power	Bias (SD)
BRAR(1/2,σ²)	δ₁=δ₂=0	P₁=P₂=0.3	290(47)	0.50(0.10)	0.07	0(0.082)
	δ₁=δ₂=0	P₁=0.5, P₂=0.3	157(86)	0.63(0.08)	0.858	0.046(0.097)
	δ₁=δ₂=0.1	P₁=P₂=0.3	288(51)	0.50(0.10)	0.091	0(0.091)
	δ₁=δ₂=0.1	P₁=0.5, P₂=0.3	150(83)	0.62(0.08)	0.89	0.054(0.096)
	δ₁=δ₂=0.2	P₁=P₂=0.3	284(55)	0.50(0.10)	0.122	0(0.101)
	δ₁=δ₂=0.2	P₁=0.5, P₂=0.3	144(78)	0.62(0.08)	0.919	0.059(0.091)
Time-trend Adjusted Randomization (TTAR)	δ₁=δ₂=0.1	P₁=P₂=0.3	292(41)	0.50 (0.11)	0.06	0 (0.087)
	δ₁=δ₂=0.1	P₁=0.5, P₂=0.3	184(84)	0.64(0.07)	0.815	0.049(0.092)
	δ₁=δ₂=0.2	P₁=P₂=0.3	291(46)	0.49 (0.09)	0.067	0(0.092)
	δ₁=δ₂=0.2	P₁=0.5, P₂=0.3	185(86)	0.64(0.07)	0.783	0.054(0.093)