TY - JOUR
T1 - Design and inference for 3-stage bioequivalence testing with serial sampling data
AU - Yan, Fangrong
AU - Zhu, Huihong
AU - Liu, Junlin
AU - Jiang, Liyun
AU - Huang, Xuelin
N1 - Funding Information:
The research of F.Y. was supported by National Social Science Foundation of China (no. 16BTJ021). The research of X.H. was supported in part by USA National Institutes of Health (NIH) grants U54 CA096300, U01 CA152958, and 5P50 CA100632.
Publisher Copyright:
© 2018 John Wiley & Sons, Ltd.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - A bioequivalence test is to compare bioavailability parameters, such as the maximum observed concentration (Cmax) or the area under the concentration-time curve, for a test drug and a reference drug. During the planning of a bioequivalence test, it requires an assumption about the variance of Cmax or area under the concentration-time curve for the estimation of sample size. Since the variance is unknown, current 2-stage designs use variance estimated from stage 1 data to determine the sample size for stage 2. However, the estimation of variance with the stage 1 data is unstable and may result in too large or too small sample size for stage 2. This problem is magnified in bioequivalence tests with a serial sampling schedule, by which only one sample is collected from each individual and thus the correct assumption of variance becomes even more difficult. To solve this problem, we propose 3-stage designs. Our designs increase sample sizes over stages gradually, so that extremely large sample sizes will not happen. With one more stage of data, the power is increased. Moreover, the variance estimated using data from both stages 1 and 2 is more stable than that using data from stage 1 only in a 2-stage design. These features of the proposed designs are demonstrated by simulations. Testing significance levels are adjusted to control the overall type I errors at the same level for all the multistage designs.
AB - A bioequivalence test is to compare bioavailability parameters, such as the maximum observed concentration (Cmax) or the area under the concentration-time curve, for a test drug and a reference drug. During the planning of a bioequivalence test, it requires an assumption about the variance of Cmax or area under the concentration-time curve for the estimation of sample size. Since the variance is unknown, current 2-stage designs use variance estimated from stage 1 data to determine the sample size for stage 2. However, the estimation of variance with the stage 1 data is unstable and may result in too large or too small sample size for stage 2. This problem is magnified in bioequivalence tests with a serial sampling schedule, by which only one sample is collected from each individual and thus the correct assumption of variance becomes even more difficult. To solve this problem, we propose 3-stage designs. Our designs increase sample sizes over stages gradually, so that extremely large sample sizes will not happen. With one more stage of data, the power is increased. Moreover, the variance estimated using data from both stages 1 and 2 is more stable than that using data from stage 1 only in a 2-stage design. These features of the proposed designs are demonstrated by simulations. Testing significance levels are adjusted to control the overall type I errors at the same level for all the multistage designs.
KW - Bioequivalence testing
KW - Sample size estimation
KW - Sequential design
KW - Serial sampling data
KW - Statistical power
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U2 - 10.1002/pst.1865
DO - 10.1002/pst.1865
M3 - Article
C2 - 29726096
AN - SCOPUS:85046350629
SN - 1539-1604
VL - 17
SP - 458
EP - 476
JO - Pharmaceutical statistics
JF - Pharmaceutical statistics
IS - 5
ER -