Sample size per sequence for responses with <i>α</i> = 0.05 and power = 0.8 under constant-scaled criterion.

<p>Sample size per sequence for responses with <i>α</i> = 0.05 and power = 0.8 under constant-scaled criterion.</p

Specifications of parameters for simulation studies.

<p>Specifications of parameters for simulation studies.</p

Sample size per sequence for responses with <i>α</i> = 0.05 and power = 0.8 under reference-scaled criterion.

<p>Sample size per sequence for responses with <i>α</i> = 0.05 and power = 0.8 under reference-scaled criterion.</p

Sample size determination for a specific region in multiregional clinical trials with multiple co-primary endpoints

<div><p>Recently, multi-regional clinical trials (MRCTs), which incorporate subjects from many countries/regions around the world under the same protocol, have been widely conducted by many global pharmaceutical companies. The objective of such trials is to accelerate the development process for a drug and shorten the drug’s approval time in key markets. Several statistical methods have been purposed for the design and evaluation of MRCTs, as well as for assessing the consistency of treatment effects across all regions with one primary endpoint. However, in some therapeutic areas (e.g., Alzheimer’s disease), the clinical efficacy of a new treatment may be characterized by a set of possibly correlated endpoints, known as multiple co-primary endpoints. In this paper, we focus on a specific region and establish three statistical criteria for evaluating consistency between the specific region and overall results in MRCTs with multiple co-primary endpoints. More specifically, two of those criteria are used to assess whether the treatment effect in the region of interest is as large as that of the other regions or of the regions overall, while the other criterion is used to assess the consistency of the treatment effect of the specific region achieving a pre-specified threshold. The sample size required for the region of interest can also be evaluated based on these three criteria.</p></div

Sample size and assurance probabilities for observing criteria (i), (ii), and (iii) given <i>α</i> = 0.025, <i>β</i> = 0.1, (Δ₁, Δ₂) = (3,0.45), (<i>σ</i>₁, <i>σ</i>₂) = (6,1), (<i>γ</i>₁, <i>γ</i>₂) = (0.5, 0.5), and <i>ρ</i>₁₂ = 0.5.

<p>Sample size and assurance probabilities for observing criteria (i), (ii), and (iii) given <i>α</i> = 0.025, <i>β</i> = 0.1, (Δ₁, Δ₂) = (3,0.45), (<i>σ</i>₁, <i>σ</i>₂) = (6,1), (<i>γ</i>₁, <i>γ</i>₂) = (0.5, 0.5), and <i>ρ</i>₁₂ = 0.5.</p

A Tolerance Interval Approach to Assessing the Biosimilarity of Follow-On Biologics

<p>With many important biologic products due to lose patent protection in the next few years, the development of follow-on biologics has received much attention from both sponsors and regulatory authorities. Biologics are often produced in living systems. The living systems used to produce biologics are highly complex and could be sensitive to very minor changes in the manufacturing process. According to the guideline published by the European Medicines Agency, biosimilar products are similar, not identical, to the innovator products they seek to copy. Therefore, in developing a biosimilar, it is important to assess the similarity between it and the innovator product. In this article, we consider a two-arm, parallel design with a reference biological product and a biosimilar. Then we construct a biosimilarity index for assessing the degree of similarity based on the tolerance limits. The acceptance criterion is proposed to judge whether the biosimilar is similar to the reference product. We also address the determination of the number of subjects to ensure that the occurring probability of biosimilarity criterion is maintained at a desired level, say 80 or 90%.</p

Scree plot.

<p>Eigenvalue ratio of 1,000 bootstrapped samples.</p

33 table of LEPR.12 vs PRKCZ.14 (mutual information: 0.0018).

<p>33 table of LEPR.12 vs PRKCZ.14 (mutual information: 0.0018).</p

Cluster statistics based on B-F-W tests.

<p>*Cluster1 and Cluster2 have 177 and 380 individuals, respectively.</p

Eleven measurements relevant to insulin resistance.

<p>Eleven measurements relevant to insulin resistance.</p