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Design and inference for 3‐stage bioequivalence testing with serial sampling data
Author(s) -
Yan Fangrong,
Zhu Huihong,
Liu Junlin,
Jiang Liyun,
Huang Xuelin
Publication year - 2018
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1865
Subject(s) - bioequivalence , statistics , sample size determination , variance (accounting) , mathematics , sampling (signal processing) , stage (stratigraphy) , sampling design , inference , sample (material) , statistical hypothesis testing , computer science , econometrics , bioavailability , artificial intelligence , bioinformatics , population , chemistry , demography , accounting , filter (signal processing) , chromatography , sociology , business , computer vision , biology , paleontology
A bioequivalence test is to compare bioavailability parameters, such as the maximum observed concentration ( C max ) 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 C max 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.