| Type: | Package |
| Title: | Screened Selection Design with Binary Endpoints |
| Version: | 0.1.0 |
| Maintainer: | Chia-Wei Hsu <Chia-Wei.Hsu@stjude.org> |
| Description: | A study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the response rate) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. The proposed designs can “partially” control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal_2arm_binary() or optimal_3arm_binary() function) for each arm. All the design needed components (sample size, operating characteristics) are supported. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| Depends: | mvtnorm, clinfun, ph2mult |
| NeedsCompilation: | no |
| Packaged: | 2024-06-26 03:27:31 UTC; chsu1 |
| Author: | Chia-Wei Hsu [aut, cre], Zongheng Cai [aut], Haitao Pan [aut] |
| Repository: | CRAN |
| Date/Publication: | 2024-06-26 13:00:06 UTC |
Generate operating characteristics for Two-Stage Screened Selection Design for Randomized Phase II Trials with Binary Endpoints
Description
Obtain the operating characteristics of Two-Stage Screened Selection Design for Randomized Phase II Trials with Binary Endpoints. The arguments for this function are from outputs of the functions of sample_size_2arm_binary() and optimal_2arm_binary()
Usage
get_oc_2arm_binary(r1, r2, n1, n, p0, p1, p2, diff = 0, nsim, seed = 2483)
Arguments
r1 |
the maximum number of successes in stage 1 which will terminate trial |
r2 |
the maximum number of successes in stage 2 not to warrant further investigation |
n1 |
the number of subjects in stage 1 |
n |
the total number of subjects (stage 1 + stage 2) |
p0 |
the response rate of historical data |
p1 |
the response rate of arm 1 |
p2 |
the response rate of arm 2 |
diff |
the equivalence margin |
nsim |
the number of simulated trials |
seed |
the seed. The default value is seed = 2483 |
Value
get_oc_2arm_binary() returns: (1) n: total sample size for each arm (2) SSD.Arm.A: selection probability of Arm A (3) SSD.Arm.B: selection probability of Arm B (4) SSD.No.Arm: the probability of no arms selected (5) diff: the equivalence margin (6) Mean.N.Arm.A: the average number of patients allocated to Arm A (7) Mean.N.Arm.B: the average number of patients allocated to Arm B
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
get_oc_2arm_binary(r1 = 2, r2 = 6, n1 = 11, n = 21, p0 = 0.2,
p1 = 0.415, p2 = 0.615, nsim = 100)
Generate operating characteristics for Two-Stage Screened Selection Design for Randomized Phase II Trials with Binary Endpoints for 3 arms
Description
Obtain the operating characteristics of Two-Stage Screened Selection Design for Randomized Phase II Trials with Binary Endpoints for 3 arms. The arguments for this function are from outputs of the functions of sample_size_3arm_binary() and optimal_3arm_binary()
Usage
get_oc_3arm_binary(r1, r2, n1, n, p0, p1, p2, p3, diff = 0, nsim, seed = 2483)
Arguments
r1 |
the maximum number of successes in stage 1 which will terminate trial |
r2 |
the maximum number of successes in stage 2 not to warrant further investigation |
n1 |
the number of subjects in stage 1 |
n |
the total number of subjects (stage 1 + stage 2) |
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 |
diff |
the equivalence margin. The default value is diff = 0 |
nsim |
the number of simulated trials |
seed |
the seed. The default value is seed = 2483 |
Value
get_oc_3arm_binary() returns: (1) n: total sample size for each arm (2) SSD.Arm.A: selection probability of Arm A (3) SSD.Arm.B: selection probability of Arm B (4) SSD.Arm.C: selection probability of Arm C (5) SSD.No.Arm: the probability of no arms selected (6) diff: the equivalence margin (7) Mean.N.Arm.A: the average number of patients allocated to Arm A (8) Mean.N.Arm.B: the average number of patients allocated to Arm B (9) Mean.N.Arm.C: the average number of patients allocated to Arm C
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
get_oc_3arm_binary(r1 = 4, r2 = 25, n1 = 15, n = 82,
p0 = 0.2, p1 = 0.415, p2 = 0.515,
p3 = 0.615, nsim = 100)
Find optimal design parameters
Description
Find the optimal parameters used in the get_oc_2arm() function
Usage
optimal_2arm_binary(p0, p1, p2, 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 |
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 |
Value
optimal_2arm_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_2arm_binary(p0 = 0.2, p1 = 0.415, p2 = 0.615, tot_sample = 21)
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 |
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)
Calculate the sample size for each arm in a two-arm trial
Description
Calculate the sample size for each arm in a two-arm trial
Usage
sample_size_2arm_binary(p0, p1, p2, diff = 0, selection.prob = 0.9,
alpha = 0.1, beta = 0.2)
Arguments
p0 |
the successful probability of historical data |
p1 |
the response rate of arm 1 |
p2 |
the response rate of arm 2 |
diff |
the equivalence margin |
selection.prob |
the probability of selection of a superior arm. The default value is selection.prob = 0.9 |
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 |
Value
sample_size_2arm_binary() returns required sample size for each arm
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
sample_size_2arm_binary(p0 = 0.2, p1 = 0.415, p2 = 0.615)
Calculate the sample size for each arm in a three-arm study
Description
Calculate the sample size for each arm in a three-arm trial
Usage
sample_size_3arm_binary(p0, p1, p2, p3, diff = 0, selection.prob = 0.9, alpha = 0.1,
beta = 0.2)
Arguments
p0 |
the response rate of historical control arm |
p1 |
the response rate of arm 1 |
p2 |
the response rate of arm 2 |
p3 |
the response rate of arm 3 |
diff |
the equivalence margin. The default value is diff = 0 |
selection.prob |
the probability of selection of a superior arm. The default value is selection.prob = 0.9 |
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 |
Value
sample_size_3arm_binary() returns required sample size for each arm
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
sample_size_3arm_binary(p0 = 0.2, p1 = 0.415, p2 = 0.515, p3 = 0.615)