The similarity factor f2 is a simple measure for the comparison of two dissolution profiles. But the commonly used similarity factor estimate f2 is a biased and conservative estimate of f2. The bootstrap approach is a useful tool to simulate the confidence interval.

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Interpretation of subgroup findings is a difficult task. The attempt of this article is to clarify confusions on subgroup analysis and to give some practical suggestions on how to avoid mistakes in interpreting subgroup outcome. We believe that the correct interpretation of subgroup findings is closely related to the intrinsic statistical property and validity of the subgroup analysis. A systematic discussion on subgroup analysis from a statistical point of view will be helpful to clinical trial practitioners.

The specificity and temporality of the observed adverse manifestations suggest a causal relationship to enalapril exposure.

Bioequivalence between two treatments or two drugs is ofren assessed by comparing the two proportions (success rate or eradication rate) of binomial outcomes when the conventional pharmacokinetic parameters are inadequate for the assessment. Setting the equivalence limits can be based on one of the three measures: difference, ratio, or odds ratio between the two binomial probabilities. This paper reviews the existing asymptotic test statistics for comparing two independent binomial probabilities in terms of the three measures in the context of equivalence or noninferiority testing. The actual type I error and power of the asymptotic tests are evaluated by enumerating the exact probabilities in the rejection region. The results show that to establish an equivalence between two treatments with an equivalence limit of 20% in difference, a sample size of at least 50 per treatment is needed. When the sample size is sufficient, the actual type I error rate is close to the nominal level (slightly above the nominal level in several cases) for a test in terms of difference for equivalence limits, and it tends to exceed the nominal level for tests in terms of ratio or odds ratio.

In a group sequential active controlled clinical trial, the study hypothesis may be a superiority hypothesis that an experimental treatment is more e ective than the active control therapy or a non-inferiority hypothesis that the treatment is no worse than the active control within some non-inferiority range. When it is necessary to plan for testing the superiority and the non-inferiority hypotheses, we propose an adaptive group sequential closed test strategy by which the sample size is planned for testing superiority and is to be increased for showing non-inferiority given that it is deemed more plausible than superiority based on the observed sample path during the course of the trial. The proposed adaptive test strategy is valid in terms of having the type I error probability maintained at the targeted level for both superiority and non-inferiority. It has power advantage or sample size saving over the traditional group sequential test designed for testing either superiority only or non-inferiority only.