Study Of The Homogeneity Of Drug Loaded In Polymeric Films Using Near-infrared Chemical Imaging And Split-plot Design.

Split-plot design (SPD) and near-infrared chemical imaging were used to study the homogeneity of the drug paracetamol loaded in films and prepared from mixtures of the biocompatible polymers hydroxypropyl methylcellulose, polyvinylpyrrolidone, and polyethyleneglycol. The study was split into two parts: a partial least-squares (PLS) model was developed for a pixel-to-pixel quantification of the drug loaded into films. Afterwards, a SPD was developed to study the influence of the polymeric composition of films and the two process conditions related to their preparation (percentage of the drug in the formulations and curing temperature) on the homogeneity of the drug dispersed in the polymeric matrix. Chemical images of each formulation of the SPD were obtained by pixel-to-pixel predictions of the drug using the PLS model of the first part, and macropixel analyses were performed for each image to obtain the y-responses (homogeneity parameter). The design was modeled using PLS regression, allowing only the most relevant factors to remain in the final model. The interpretation of the SPD was enhanced by utilizing the orthogonal PLS algorithm, where the y-orthogonal variations in the design were separated from the y-correlated variation.

Efecto de la violaci??n de la normalidad y esfericidad en el modelo lineal mixto en dise??os split-plot

Resumen tomado de la publicaci??n

Split-mouth designs in orthodontics: an overview with applications to orthodontic clinical trials

SUMMARY Split-mouth designs first appeared in dental clinical trials in the late sixties. The main advantage of this study design is its efficiency in terms of sample size as the patients act as their own controls. Cited disadvantages relate to carry-across effects, contamination or spilling of the effects of one intervention to another, period effects if the interventions are delivered at different time periods, difficulty in finding similar comparison sites within patients and the requirement for more complex data analysis. Although some additional thought is required when utilizing a split-mouth design, the efficiency of this design is attractive, particularly in orthodontic clinical studies where carry-across, period effects and dissimilarity between intervention sites does not pose a problem. Selection of the appropriate research design, intervention protocol and statistical method accounting for both the reduced variability and potential clustering effects within patients should be considered for the trial results to be valid.

Statnote 12: the split-plot analysis of variance

In some experimental situations, the factors may not be equivalent to each other and replicates cannot be assigned at random to all treatment combinations. A common case, called a ‘split-plot design’, arises when one factor can be considered to be a major factor and the other a minor factor. Investigators need to be able to distinguish a split-plot design from a fully randomized design as it is a common mistake for researchers to analyse a split-plot design as if it were a fully randomised factorial experiment.

ANÁLISE DE REGRESSÃO em EXPERIMENTOS em PARCELAS SUBDIVIDIDAS COM DADOS NÃO-BALANCEADOS: UMA SOLUÇÃO NA LINGUAGEM GLIM

São estabelecidas as matrizes necessárias para a realização da análise de variância de experimentos em parcelas subdivididas, com dados não-balanceados e balanceados, quando os tratamentos aplicados às parcelas e os tratamentos aplicados às subparcelas são ambos fatores quantitativos, usando a teoria de modelos lineares e de modelos lineares generalizados. Foi desenvolvido um programa computacional, na linguagem GLIM, para a realização da análise.

Levene tests of homogeneity of variance for general block and treatment designs

This article develops a weighted least squares version of Levene's test of homogeneity of variance for a general design, available both for univariate and multivariate situations. When the design is balanced, the univariate and two common multivariate test statistics turn out to be proportional to the corresponding ordinary least squares test statistics obtained from an analysis of variance of the absolute values of the standardized mean-based residuals from the original analysis of the data. The constant of proportionality is simply a design-dependent multiplier (which does not necessarily tend to unity). Explicit results are presented for randomized block and Latin square designs and are illustrated for factorial treatment designs and split-plot experiments. The distribution of the univariate test statistic is close to a standard F-distribution, although it can be slightly underdispersed. For a complex design, the test assesses homogeneity of variance across blocks, treatments, or treatment factors and offers an objective interpretation of residual plot.

Multistratum response surface designs

Response surface designs are usually described as if the treatments have been completely randomized to the experimental units. However, in practice there is often a structure to the units, implying the need for blocking. If, in addition, some factors are more difficult to vary between units than others, a multistratum structure arises naturally. We present a general strategy for constructing response surface designs in multistratum unit structures. Designs are constructed stratum by stratum, starting in the highest stratum. In each stratum a prespecified treatment set for the factors applied in that stratum is arranged to be nearly orthogonal to the units in the higher strata, allowing-for all the effects that have to be estimated. Three examples are given to show the applicability of the method and are also used to check the relationship of the final design to the choice of treatment set. Finally, some practical considerations in randomization are discussed.

The application of analysis of variance (ANOVA) to different experimental designs in optometry

Analysis of variance (ANOVA) is the most efficient method available for the analysis of experimental data. Analysis of variance is a method of considerable complexity and subtlety, with many different variations, each of which applies in a particular experimental context. Hence, it is possible to apply the wrong type of ANOVA to data and, therefore, to draw an erroneous conclusion from an experiment. This article reviews the types of ANOVA most likely to arise in clinical experiments in optometry including the one-way ANOVA ('fixed' and 'random effect' models), two-way ANOVA in randomised blocks, three-way ANOVA, and factorial experimental designs (including the varieties known as 'split-plot' and 'repeated measures'). For each ANOVA, the appropriate experimental design is described, a statistical model is formulated, and the advantages and limitations of each type of design discussed. In addition, the problems of non-conformity to the statistical model and determination of the number of replications are considered. © 2002 The College of Optometrists.

Utilização de técnicas estatísticas em duas revistas de fruticultura

Neste trabalho são descritas as técnicas de análise estatística utilizadas e a acessibilidade estatística em uma amostra dos artigos originais publicados no período 1996-2006 em duas revistas de pesquisa na área de fruticultura: a Revista Brasileira de Fruticultura (RBF) e a revista francesa Fruits. No total foram classificados 986 artigos em 16 categorias de análise estatística, ordenadas em grau ascendente de complexidade. No período analisado, foi constatado um aumento no uso de análises mais sofisticadas ao longo do tempo em ambos as revistas. Os trabalhos publicados pela RBF aplicaram com maior freqüência técnicas estatísticas mais complexas, com maior utilização de delineamentos em blocos aleatorizados, arranjos fatoriais, parcelas subdivididas e modelos hierárquicos, e do teste de Tukey para comparações múltiplas de médias. Nos trabalhos publicados pela revista Fruits, predominou o uso de outros testes paramétricos e do teste de Duncan. O pacote estatístico SAS foi o mais utilizado nos artigos publicados em ambas as revistas. Os leitores da revista RBF precisaram de um nível de conhecimento estatístico mais elevado para ter acesso à maior parte dos artigos publicados no período, em comparação com os leitores da revista francesa.

Ensaios em parcelas subdivididas delineados em blocos incompletos balanceados. I - Equações normais

Com o objetivo de ampliar o uso dos ensaios com parcelas subdivididas na pesquisa agropecuária, realizou-se um estudo de tais ensaios delineados em blocos incompletos balanceados. Adotou-se, para tanto, o modelo tradicionalmente usado no delineamento completo. Optou-se pela existência de correlação constante entre subparcelas distintas. A obtenção das estimativas para efeitos de blocos ocorreu como nos ensaios em blocos incompletos balanceados, enquanto que as estimativas para efeitos de tratamentos secundários e para a interação tratamentos principais x tratamentos secundários portaram-se como nos ensaios com parcelas subdivididas em blocos (completos), casualizados.

Statnote 13: a more complex analysis of variance incorporating a 'repeated measures' factor

Experiments combining different groups or factors and which use ANOVA are a powerful method of investigation in applied microbiology. ANOVA enables not only the effect of individual factors to be estimated but also their interactions; information which cannot be obtained readily when factors are investigated separately. In addition, combining different treatments or factors in a single experiment is more efficient and often reduces the sample size required to estimate treatment effects adequately. Because of the treatment combinations used in a factorial experiment, the degrees of freedom (DF) of the error term in the ANOVA is a more important indicator of the ‘power’ of the experiment than the number of replicates. A good method is to ensure, where possible, that sufficient replication is present to achieve 15 DF for the error term of the ANOVA testing effects of particular interest. Finally, it is important to always consider the design of the experiment because this determines the appropriate ANOVA to use. Hence, it is necessary to be able to identify the different forms of ANOVA appropriate to different experimental designs and to recognise when a design is a split-plot or incorporates a repeated measure. If there is any doubt about which ANOVA to use in a specific circumstance, the researcher should seek advice from a statistician with experience of research in applied microbiology.

Data methods in optometry Part 9: experimental design and analysis of variance

The key to the correct application of ANOVA is careful experimental design and matching the correct analysis to that design. The following points should therefore, be considered before designing any experiment: 1. In a single factor design, ensure that the factor is identified as a 'fixed' or 'random effect' factor. 2. In more complex designs, with more than one factor, there may be a mixture of fixed and random effect factors present, so ensure that each factor is clearly identified. 3. Where replicates can be grouped or blocked, the advantages of a randomised blocks design should be considered. There should be evidence, however, that blocking can sufficiently reduce the error variation to counter the loss of DF compared with a randomised design. 4. Where different treatments are applied sequentially to a patient, the advantages of a three-way design in which the different orders of the treatments are included as an 'effect' should be considered. 5. Combining different factors to make a more efficient experiment and to measure possible factor interactions should always be considered. 6. The effect of 'internal replication' should be taken into account in a factorial design in deciding the number of replications to be used. Where possible, each error term of the ANOVA should have at least 15 DF. 7. Consider carefully whether a particular factorial design can be considered to be a split-plot or a repeated measures design. If such a design is appropriate, consider how to continue the analysis bearing in mind the problem of using post hoc tests in this situation.

Dentin Bond Strength Of A Fluoride-releasing Adhesive System Submitted To Ph-cycling.

To evaluate the microtensile bond strength (µTBS) of a fluoride-containing adhesive system submitted to a pH-cycling and storage time regimen for primary outcomes. As secondary outcomes the fluoride released amount was evaluated. Twelve dentin surfaces from sound third molar were divided into 2 groups according to adhesive systems: Clearfil SE Protect (PB) and Clearfil SE Bond (SE). Sticks obtained (1.0 mm2) from teeth were randomly divided into 3 subgroups according to storage regimen model: immediate (24h); 5-month deionized water (W); and pH-cycling model (C). All sticks were tested for µTBS in a universal testing machine. Fluoride concentration was obtained from 1-4 days and 30-day in W and 1-4 days in demineralization (DE)/remineralization (RE) solutions from C, using a fluoride-specific electrode. µTBS and fluoride released data were, respectively, submitted to ANOVA in a split plot design and Tukey, and Friedman' tests (a=0.05). There was no significant interaction between adhesive system and storage regimen for µTBS. W showed the lowest µTBS values. There was no significant difference between 24 h and C models for µTBS. There was no significant difference between adhesive systems. Failure mode was predominantly cohesive within composite for the 24 h and W, for the C group it was mixed for SE and cohesive within composite for PB adhesive system. Fluoride concentrations in the DE/RE solutions were less than 0.03125 ppm and not detected in W. In conclusion, the fluoride-containing adhesive system performed similarly to the regular one. Hydrolytic degradation is the main problem with both adhesive systems, regardless of fluoride contents.

Conseqüências da análise incorreta de experimentos blocados

Errors are always present in experimental measurements so, it is important to identify them and understand how they affect the results of experiments. Statistics suggest that the execution of experiments should follow random order, but unfortunately the complete randomization of experiments is not always viable for practical reasons. One possible simplification is blocked experiments within which the levels of certain factors are maintained fixed while the levels of others are randomized. However this has a cost. Although the experimental part is simplified, the statistical analysis becomes more complex.