optimal_3arm_binary {frequentistSSDBinary}

Find optimal design parameters

Description

Find the optimal parameters used in the get_oc_3arm_binary() function

Usage

optimal_3arm_binary(p0, p1, p2, p3, alpha = 0.1, beta = 0.2, tot_sample)

Arguments

p0

the response rate of historical data

p1

the response rate of arm 1

p2

the response rate of arm 2

p3

the response rate of arm 3

alpha

the type I error to be controlled. The default value is alpha = 0.1

beta

the type II error to be controlled. The default value is beta = 0.2

tot_sample

the required sample size for each arm from function sample_size_3arm_binary()

Value

optimal_3arm_binary() returns: (1) alpha: type I error (2) beta: typeII error (3) r1: the maximum number of successes in stage 1 which will terminate trial (4) n1: the number of subjects in stage 1 (5) r2: the maximum number of successes in stage 2 not to warrant further investigation (6) n: the total number of subjects (stage 1 + stage 2) (7) ESS: the expected sample size for each arm (8) PS:the probability of early stopping

Author(s)

Chia-Wei Hsu, Zongheng Cai, Haitao Pan

References

Cai, Z., Pan, H., Wu, J., Hsu, C.W. (2024). Uncontrolled Randomized Screening Selection Design for Pediatric Oncology Trials. Accepted in Book Chapter of "Master Protocol Clinical Trial for Efficient Evidence Generation"

Wu, J., Pan, H., & Hsu, C. W. (2022). Two-stage screened selection designs for randomized phase II trials with time-to-event endpoints. Biometrical Journal, 64(7), 1207-1218

Yap, C., Pettitt, A. & Billingham, L. Screened selection design for randomised phase II oncology trials: an example in chronic lymphocytic leukaemia. BMC Med Res Methodol 13, 87 (2013)

Examples

optimal_3arm_binary(p0 = 0.2, p1 = 0.415, p2 = 0.515, p3 = 0.615,
                    alpha = 0.1, beta = 0.2, tot_sample = 82)