Distances between populations ofDrosophila subobscura, based on differences in the frequencies of chromosomal arrangements have been estimated using data from about 65 populations. The distances have been calculated using the formula:[Formula: see text] wherer is the number of loci or chromosomes (in the case of chromosomal polymorphism) considered,p 1jk the frequency of the allele or chromosomal arrangementk in the locus or chromosomej in the first population, andp zjk the corrsponding value in the second population.The main conclusion drawn from this analysis is that historical as well as adaptive factors are important in explaining the geographical distribution of chromosomal arrangements inD. subobscura. In general, isolated populations maintain primitive features in their chromosomal polymorphism. This is reflected in a tendency to similarity between these populations. Also, a very sharp effect of geographical barriers is detected in the distribution of the chromosomal arrangements.Two main factors are considered responsible for the strong effect that isolation has on geographical distribution of chromosome arrangements. These factors are the non-recurrence of rearrangements and the difficulty of establishing in one population the supergenes originated in another area, because of lack of coadaptation with the gene pool of the recipient population.
In an attempt to investigate the relationships between allometry and locomotory adaptations, we studied the long limb bones of 45 species of insectivores and rodents. Animals ranged from a few grams to about 50 kilograms. Diameter and length of the bones and body mass (when known) were recorded. Regressions of diameter to length, diameter to body mass, and length to body mass were calculated by the least-squares and Model II, or major axis, methods. The results obtained do not agree with the predictions of either the theory of geometric similarity or the theory of elastic similarity. The discrepancies could be due to the fact that animals studied exhibit various modes of locomotion. Moreover, the allometric relationships of the different locomotor patterns are better reflected in insectivores and rodents than in other groups of mammals. The use of a single regression analysis seems to be inadequate when the sample includes a large range of body sizes.
The 2010 US Food and Drug Administration and European Medicines Agency regulatory approaches to establish bioequivalence in highly variable drugs are both based on linearly scaling the bioequivalence limits, both take a 'scaled average bioequivalence' approach. The present paper corroborates previous work suggesting that none of them adequately controls type I error or consumer's risk, so they result in invalid test procedures in the neighbourhood of a within-subject coefficient of variation osf 30% for the reference (R) formulation. The problem is particularly serious in the US Food and Drug Administration regulation, but it is also appreciable in the European Medicines Agency one. For the partially replicated TRR/RTR/RRT and the replicated TRTR/RTRT crossover designs, we quantify these type I error problems by means of a simulation study, discuss their possible causes and propose straightforward improvements on both regulatory procedures that improve their type I error control while maintaining an adequate power. Copyright © 2015 John Wiley& Sons, Ltd.
Reference-scaled average bioequivalence (RSABE) approaches for highly variable drugs are based on linearly scaling the bioequivalence limits according to the reference formulation within-subject variability. RSABE methods have type I error control problems around the value where the limits change from constant to scaled. In all these methods, the probability of type I error has only one absolute maximum at this switching variability value. This allows adjusting the significance level to obtain statistically correct procedures (that is, those in which the probability of type I error remains below the nominal significance level), at the expense of some potential power loss. In this paper, we explore adjustments to the EMA and FDA regulatory RSABE approaches, and to a possible improvement of the original EMA method, designated as HoweEMA. The resulting adjusted methods are completely correct with respect to type I error probability. The power loss is generally small and tends to become irrelevant for moderately large (affordable in real studies) sample sizes. KEYWORDSconfidence interval inclusion principle, point estimate constraint, scaled average bioequivalence INTRODUCTIONGeneric drug products contain the same active substance as brand drugs, but under a different formulation. Provided that the test T (generic) formulation and the reference R (brand, innovator) formulation contain an equal quantity of the active substance (whose safety and therapeutic value was already demonstrated through a long and expensive clinical trial), the bioequivalence between them is defined in terms relative rate and absorption of the active substance, as judged by comparing plasma concentration curves after a single administration of T or R. For each subject participating in the study, and for each administration, these concepts are characterized by means of variables like the area under the curve (AUC) or the maximum concentration reached (Cmax), both of which are computed from the resulting plasma concentration vs time curve. According to the criteria of regulatory agencies like the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA), bioequivalence holds when the ratio of the geometric means of the bioavailabilities of T and R falls within the interval 0.80 to 1.25 (=1/0.80). This criterion is usually expressed in logarithmic scale. Then, for variables like log(AUC) or log(Cmax), the difference in means between R and T (the formulation effect, ) must be within the limits ±0.223, where 0.223 = log(1.25) = − log(0.80). In inferential statistical terms, to demonstrate bioequivalence is assimilated to rejecting a null hypothesis of bioinequivalence in favour of an alternative of bioequivalence: H 0 ∶ ≤ −0.223 ∨ ≥ 0.223 H 1 ∶ −0.223 < < 0.223.(1) Pharmaceutical Statistics. 2019;18:583-599. wileyonlinelibrary.com/journal/pst
BackgroundHow to compare studies on the basis of their biological significance is a problem of central importance in high-throughput genomics. Many methods for performing such comparisons are based on the information in databases of functional annotation, such as those that form the Gene Ontology (GO). Typically, they consist of analyzing gene annotation frequencies in some pre-specified GO classes, in a class-by-class way, followed by p-value adjustment for multiple testing. Enrichment analysis, where a list of genes is compared against a wider universe of genes, is the most common example.ResultsA new global testing procedure and a method incorporating it are presented. Instead of testing separately for each GO class, a single global test for all classes under consideration is performed. The test is based on the distance between the functional profiles, defined as the joint frequencies of annotation in a given set of GO classes. These classes may be chosen at one or more GO levels. The new global test is more powerful and accurate with respect to type I errors than the usual class-by-class approach. When applied to some real datasets, the results suggest that the method may also provide useful information that complements the tests performed using a class-by-class approach if gene counts are sparse in some classes. An R library, goProfiles, implements these methods and is available from Bioconductor, http://bioconductor.org/packages/release/bioc/html/goProfiles.html.ConclusionsThe method provides an inferential basis for deciding whether two lists are functionally different. For global comparisons it is preferable to the global chi-square test of homogeneity. Furthermore, it may provide additional information if used in conjunction with class-by-class methods.
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