Subject-by-formulation interaction in determinations of individual bioequivalence: Bias and prevalence
Subject-by-formulation interaction in determinations of individual bioequivalence: Bias and prevalence
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DOI：
10.1023/a:1018899504711
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发表时间：
1999-02-01
影响因子：
3.7
通讯作者：
Tothfalusi, L
中科院分区：
文献类型：
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作者：
Endrenyi, L;Tothfalusi, L
目的。 1. 确定个体生物等效性 (IBE) 调查中受试者与制剂相互作用 (sigma(D)(2)) 的估计方差分量的特性，以及 2. 评估 FDA.Methods 发表的重复设计研究中相互作用的普遍性。评估 IBE 的四期交叉研究被反复模拟。一般来说，假设两种制剂的真实生物等效性，包括 sigma(D)(2) = 0。然后通过限制最大似然 (REML) 在线性混合效应模型中估计 sigma(D)(2)。对于 FDA.Results 的数据集，应用相同的方法来估计 a：。 1. REML 估计的 sigma(D) 存在正偏差。估计值的偏差和离散度与参考制剂的估计受试者内标准差 (sigma(WR)) 近似线性增加。只有一小部分估计的 sigma(D) 超过了估计的 sigma(WR)。 2.评估估计的sigma(D)的分布。当 sigma(WR) = 0.30 时，估计的 sigma(D) = 0.15 水平被随机超出，概率约为 25%。 3. 重要的是，从 FDA 数据集估计的 sigma(D)(2) 值的行为与在真实生物等效性条件下生成的 sigma(D)(2) 模拟估计值相似。结论。 1. REML 估计的 sigma(D) 存在偏差：偏差与估计的 sigma(WR) 成比例增加。因此，超过 sigma(D) 的固定水平（例如 0.15）并不表示存在实质性交互作用。 2. FDA 的数据集与 sigma(D)(2) = 0 的假设相一致。因此，它们没有证明受试者与制剂之间相互作用的普遍性。因此，通过两期交叉研究来评估生物等效性是充分且合理的。
Purpose. 1. To determine properties of the estimated variance component for the subject-by-formulation interaction (sigma(D)(2)) in investigations of individual bioequivalence (IBE), and 2, to evaluate the prevalence of interactions in replicate-design studies published by FDA.Methods. Four-period crossover studies evaluating IBE were simulated repeatedly. Generally, the true bioequivalence of the two formulations, including sigma(D)(2) = 0, was assumed. sigma(D)(2) was then estimated in a linear mixed-effect model by restricted maximum likelihood (REML). The same method was applied for estimating a:, for the data sets of FDA.Results. 1. sigma(D) estimated by REML was positively biased. The bias and dispersion of the estimated an increased approximately linearly with the estimated within-subject standard deviation for the reference formulation (sigma(WR)) Only a small proportion of the estimated sigma(D) exceeded the estimated sigma(WR). 2. Distributions of the estimated sigma(D) were evaluated. At sigma(WR) = 0.30, a level of estimated sigma(D) = 0.15 was exceeded, by random chance, with a probability of about 25%. 3. Importantly, the behaviour of the sigma(D)(2), values estimated from the FDA data sets was similar to that exhibited by the simulated estimates of sigma(D)(2) which were generated under the conditions of true bioequivalence.Conclusions. 1. sigma(D) estimated by REML is biased: the bias increases proportionately with the estimated sigma(WR) Consequently, exceeding a fixed level of sigma(D) (e.g., 0.15) does not indicate substantial interaction. 2. The data sets of FDA are compatible with the hypothesis of sigma(D)(2) = 0. Consequently, they do not demonstrate the prevalence of subject-by-formulation interaction. Therefore, it could be sufficient and reasonable to evaluate bioequivalence from 2-period crossover studies.