Growth charts for small sample sizes using unsupervised clustering: Application to canine early growth.

Breed-specific growth curves (GCs) are needed for neonatal puppies, but breed-specific data may be insufficient. We investigated an unsupervised clustering methodology for modeling GCs by augmenting breed-specific data with data from breeds having similar growth profiles. Puppy breeds were grouped by median growth profiles (bodyweights between birth and Day 20) using hierarchical clustering on principal components. Median bodyweights for breeds in a cluster were centered to that cluster's median and used to model cluster GCs by Generalized Additive Models for Location, Shape and Scale. These were centered back to breed growth profiles to produce cluster-scale breed GCs. The accuracy of breed-scale GCs modeled with breed-specific data only and cluster-scale breed GCs were compared when modeled from diminishing sample sizes. A complete dataset of Labrador Retriever bodyweights (birth to Day 20) was split into training (410 puppies) and test (460 puppies) datasets. Cluster-scale breed and breed-scale GCs were modelled from defined sample sizes from the training dataset. Quality criteria were the percentages of observed data in the test dataset outside the target growth centiles of simulations. Accuracy of cluster-scale breed GCs remained consistently high down to sampling sizes of three. They slightly overestimated breed variability, but centile curves were smooth and consistent with breed-scale GCs modeled from the complete Labrador Retriever dataset. At sampling sizes ≤ 20, the quality of breed-scale GCs reduced notably. In conclusion, GCs for neonatal puppies generated using a breed-cluster hybrid methodology can be more satisfactory than GCs at purely the breed level when sample sizes are small.

Between-Batch Bioequivalence (BBE): a Statistical Test to Evaluate In Vitro Bioequivalence Considering the Between-Batch Variability.

Bioequivalence testing is an essential step during the development of generic drugs. Regulatory agencies have drafted recommendations and guidelines to frame this step but without finding any consensus. Different methodologies are applied depending on the geographical region. For instance, in the EU, EMA recommends using average bioequivalence test (ABE), while in the USA, FDA recommends using population bioequivalence (PBE) test. Both methods present some limitations (e.g., when batch variability is non-negligible) making it difficult to conclude to equivalence without subsequently increasing the sample size. This article proposes an alternative method to evaluate bioequivalence: between-batch bioequivalence (BBE). It is based on the comparison between the mean difference (Reference - Test) and the Reference between-batch variability. After presenting the theoretical concepts, BBE relevance is evaluated through simulation and real case (nasal spray) studies. Simulation results showed high performance of the method based on false positive and false negative rate estimations (type I and type II errors respectively). Especially, BBE has shown significantly greater true positive rates than ABE and PBE when the Reference residual standard deviation is higher than 15%, depending on the between-batch variability and the number of batches. Finally, real case applications revealed that BBE is more efficient than ABE and PBE to demonstrate equivalence, in some well-known situations where the between-batch variability is not negligible. These results suggest that BBE could be considered as an alternative to the state-of-the-art methods allowing costless development. Graphical abstract.