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PubMed2004

Applicational possibilities of linear and non-linear (polynomial) regression and analysis of variance. III. Stability determination of pharmaceutical preparations: stability of diclofenac-sodium in Diclofen injections.

This paper presents the application of the regression analysis program and the program for comparing linear regressions (modified method for one-way, analysis of variance), writtens in BASIC program language, for instance, determination of content of Diclofenac-Sodium (active ingredient in DIKLOFEN injections, ampules á 75 mg/3 ml). Stability testing of Diclofenac-Sodium was done by isothermic method of accelerated aging at 4 different temperatures (30 degrees, 40 degrees, 50 degrees and 60 degrees C) as a function of time (4 different duration of treatment: (0-155, 0-145, 0-74 and 0-44 days). The decrease in stability (decrease in the mean value of the content of Diclofenac-Sodium (in %), at different temperatures as a function of time, is possible to describe by, linear dependance. According to the value for regression equation values, the times are assessed in which the content of Diclofenac-Sodium (in %) will decrease by 10%, of the initial value. The times are follows at 30 degrees C 761.02 days, at 40 degrees C 397.26 days, at 50 degrees C 201.96 days and at 60 degrees C 58.85 days. The estimated times (in days) in which the mean value for Diclofenac-Sodium content (in %) will by 10% of the initial values, as a junction of time, are most suitably described by 3rd order parabola. Based on the parameter values which describe the 3rd order parabola, the time was estimated in which Diclofenac-Sodium content mean value (in %) will fall by 10% of the initial one at average ambient temperatures of 20 degrees C and 25 degrees C. The times are: 1409.47 days (20 degrees C) and 1042.39 days (25 degrees C). Based on the value for Fischer's coefficien (F), the comparison of trenf of Diclofenac-Sodium content (in %) shows that, under the influence of different temperatures as a function of time, among them, depending on temperature value, there is: statistically very significant difference (P << .05) at 50 degrees C and lower toward 60 degrees C, i.e. statistically probably significant difference (P > 0.01) at 40 degrees C and lower towards 50 degrees C and there is no statistically significance difference (P >> 0.05) at 30 degrees C towards 40 degrees C.

Algorithms↗
PubMed1979

An iterative algorithm for analysis of variance.

In this paper, an iterative algorithm is proposed for computing estimates of parameters and sums of squares in non-orthogonal multivariate analysis of variance, without inverting any matrix. It is useful in the case of a large design matrix for it saves memory and computation time. It was first proposed by Stevens (1948) for 3 factors and is here generalised to any number of factors and interactions of any order. Convergence properties are studied. The more orthogonal is the design, the faster is the convergence. Several examples are provided.

Analysis of Variance↗
PubMed1981

Numerical evaluation of cytologic data. X. Introduction to multivariate analysis of variance.

Quantitative measurement on cytologic material usually involves several features for each cell. The data are thus multivariate and are represented as feature vectors. Analysis of variance on univariate data is well established for the detection of small differences between sets of data. For multivariate data, the calculating schemes are much less well known. They are presented in this paper in direct analogy to univariate procedures. The calculating schedule in a factorial design in bivariate data is demonstrated.

Analysis of Variance↗
PubMed1983

The use of multivariate analysis of variance in physiological research: the two-group case.

The advantages of performing simultaneous inferences on the means of several response variables using multivariate analysis of variance are discussed. They include control over the problem of declaring false significant differences and the ability to investigate the effects of treatment on a given response variable after eliminating variations due to other response variables. Computational equivalencies with multiple linear regression in the two-group case are also discussed.

Analysis of Variance↗
PubMed2000

Using analysis of variance (ANOVA) in rehabilitation research investigations.

The article examines the underlying assumptions, applications, and interpretations of Analysis of Variance (ANOVA) in rehabilitation research. ANOVA is presented as a widely used and highly versatile statistical tool for assessing the performance of two or more groups on a broad range of dependent variables. Examples from the contemporary rehabilitation literature are used to demonstrate how ANOVA can be applied and interpreted in a number of scientific contexts.

Journal Article↗
PubMed2005

A variance component analysis on recombination rate in the COGA pedigrees.

We estimated the crossover frequency in 1,232 gametes from 356 subjects in pedigrees from the Collaborative Study on the Genetics on Alcoholism. We examined the effect of covariates including age, ethnicity, and years with ALDX1 on recombination rate, and found a positive correlation between recombination rate and years with ALDX1. By variance-component analysis, we estimated the heritability of recombination rate to be around 0.5, and provided suggestive evidence for a locus linked to recombination rate.

Alcoholism↗
PubMed1985

Pocket calculator program of one-way analysis of variance.

This program has been written for the TI-59 pocket calculator. It uses Bartlett's test to supervise the homoscedasticity, then performs the one-way analysis of variance and multiple comparisons, namely Scheffé's test, Dunnett's test, Turkey's test and Newmann-Keuls's test.

Analysis of Variance↗
PubMedDate not supplied

Analysis of variance and Westlake's test of bioavailability data using a programmable minicalculator.

A program for the HP-41 CV calculator with adapted printer is described for the analysis of variance of bioavailability data based upon the areas under the curve measured during a two-way cross-over pharmacokinetic study of two different drug formulations. The program can also perform the test of Westlake to compute the 95% confidence interval and determine if both formulations are bioequivalent.

Analysis of Variance↗
PubMed1983

Analysis of variance tables based on experimental structure.

A stepwise procedure for obtaining the experimental structure for a particular experiment is presented together with rules for deriving the analysis-of-variance table from that structure. The procedure involves the division of the factors into groups and is essentially a generalization of the method of Nelder (1965, Proceedings of the Royal Society, Series A 283, 147-162; 1965, Proceedings of the Royal Society, Series A 283, 163-178), to what are termed 'multi-tiered' experiments. The proposed method is illustrated for a wine-tasting experiment.

Analysis of Variance↗
PubMed1981

Why covariance analysis should replace increment analysis of variance in caries studies.

Consider a caries study where experimental units are a) randomly assigned to groups, b) premeasured on DMFS, c) administered a specified treatment depending on group membership, and d) postmeasured on DMFS. Traditional analysis of these data consists of analysis of variance of the increment scores (increment ANOVA). In the place of increment ANOVA, others have suggested analysis of covariance with the postmeasure as criterion and the premeasure of covariate (ANOCOV). The present paper examines and documents the following: 1) Increment ANOVA and ANOCOV test the same null hypothesis. 2) Increment ANOVA and ANOCOV have exactly the same assumptions. 3) Increment ANOVA is usually less precise than ANOCOV. 4) The same concern for violations of assumptions must be expressed with increment ANOVA as with ANOCOV (see No. 2 above). 5) ANOCOV should replace increment ANOVA in caries studies (see points 1-4).

Analysis of Variance↗
PubMed1976

Analysis of variance and covariance for colorectal adenocarcinomas in man as a logical prelude to "staging".

This study reports initial experience at the Ellis Fischel State Cancer Hospital (EFSCH), Columbia, Missouri, with the analysis of variance and covariance as applied to the ability to predict the lethality associated with colorectal neoplasms as influenced by tumor,host, and treatment variables considered individually and as multivariate clusters. The application of the methods to the clinical setting is based on the fact that each patient has one survival time. However, in the mathematical sense, each patient is composed of many different variables interacting simultaneously in highly complex ways to determine this uniqueness. The feasibility of determining the prognostic score or weight of many variables is illustrated. The combinatory effect of these scores can provide prognostic scores for clusters of variables providing a tool of much greater specificity than is provided by traditional staging systems such as the TNM.

Adenocarcinoma↗
PubMed1985

Nested analysis of variance with autocorrelated errors.

In this paper we consider the problem where there is a randomized experimental design with several successive time measurements on each experimental unit. One approach to the analysis of such data is to treat time as the subplot treatment and to use a split-plot analysis of variance. Alternatively, the problem may be considered in a more general multivariate framework. Here we recognize the time-induced correlations and apply an autoregressive time series modelling approach. Estimation and testing are addressed. Two examples are presented to illustrate the practicality of our procedure. Some extensions are also considered briefly.

Analysis of Variance↗
PubMedDate not supplied

Non-linear regression and variance ratio analysis of time based NMR data.

Biomedical NMR experiments rely frequently on data obtained sequentially over time. A method is presented for analysis of time based NMR data, which allows modelling of continuous and discontinuous functions to observed intensity changes by non-linear regression and which uses variance ratio analysis to compare these models statistically. The method eliminates many of the usual problems in the parametric analysis of experimental values obtained at discrete time points and of comparison of the coefficients of model functions which require unsubstantiated assumptions about the distribution of parameters and ignore internal correlations which may exist between such parameters. The variance ratio method is illustrated for multiple time courses obtained with 23Na NMR of perfused rat kidney undergoing hypoxic perturbation in the presence of different treatments.

Amino Acids↗
PubMed2004

Analysis of variance components in gene expression data.

MOTIVATION: A microarray experiment is a multi-step process, and each step is a potential source of variation. There are two major sources of variation: biological variation and technical variation. This study presents a variance-components approach to investigating animal-to-animal, between-array, within-array and day-to-day variations for two data sets. The first data set involved estimation of technical variances for pooled control and pooled treated RNA samples. The variance components included between-array, and two nested within-array variances: between-section (the upper- and lower-sections of the array are replicates) and within-section (two adjacent spots of the same gene are printed within each section). The second experiment was conducted on four different weeks. Each week there were reference and test samples with a dye-flip replicate in two hybridization days. The variance components included week-to-week, animal-to-animal and between-array and within-array variances. RESULTS: We applied the linear mixed-effects model to quantify different sources of variation. In the first data set, we found that the between-array variance is greater than the between-section variance, which, in turn, is greater than the within-section variance. In the second data set, for the reference samples, the week-to-week variance is larger than the between-array variance, which, in turn, is slightly larger than the within-array variance. For the test samples, the week-to-week variance has the largest variation. The animal-to-animal variance is slightly larger than the between-array and within-array variances. However, in a gene-by-gene analysis, the animal-to-animal variance is smaller than the between-array variance in four out of five housekeeping genes. In summary, the largest variation observed is the week-to-week effect. Another important source of variability is the animal-to-animal variation. Finally, we describe the use of variance-component estimates to determine optimal numbers of animals, arrays per animal and sections per array in planning microarray experiments.

Algorithms↗
PubMed1983

[Tumor suppressing effect of systemic hyperthermia with 2350 MHz microwaves combined with 5-fluorouracil in mice].

C3H/He mice were inoculated i.p. with syngeneic MM2 tumor cells (2 X 10(6) cells/mouse), and they were subsequently treated by systemic hyperthermia with microwave irradiation (2450 MHz) alone, administration of 5-FU alone, or a combination of the both. The mice were exposed to hyperthermia for 5 minutes everyday with an output of 10, 20 or 30 watts. 5-FU was administered i.p. for 3 days at dose levels of 2.5 mg/kg, 5 mg/kg or 10 mg/kg body weight. The mean survival time and the mean relative body weight were recorded. From these data, tumor-suppressing effects of the treatments were analyzed by one-way variance analysis or two-way variance analysis. The mice treated with microwave irradiation alone or 5-FU alone survived significantly longer than the control mice receiving no treatment (P less than 0.01). However, no significant difference in mean survival time was observed between the microwave group and the 5-FU group. The mean relative body weight was similar among the microwave, the 5-FU and the control group. When the mice were treated with microwave irradiation and 5-FU, significant difference was observed in the mean survival time (P less than 0.01), and the combination of microwave irradiation with an output of 20 W and 10 mg/kg of 5-FU seemed to be optimal. Determination of an optimal combination was difficult with the mean relative body weight, because body weight decreased due to side effect of the combination therapy.

Animals↗
PubMed1992

Modelling balanced longitudinal data: maximum likelihood estimation and analysis of variance.

For linear models, assuming a within-experimental-units covariance structure that incorporates errors of measurement, serial correlation, and variation between units, results on explicit estimation of regression parameters are used to simplify maximum likelihood estimation of covariance parameters. The use of an analysis of variance table as a simpler alternative to likelihood inference is illustrated with two examples.

Analysis of Variance↗
PubMed2007

Epidemic dynamics of two coexisting hepatitis C virus subtypes.

Hepatitis C virus (HCV) infection affects about 3% of the human population. Phylogenetic analyses have grouped its variants into six major genotypes, which have a star-like distribution and several minor subtypes. The most abundant genotype in Europe is the so-called genotype 1, with two prevalent subtypes, 1a and 1b. In order to explain the higher prevalence of subtype 1b over 1a, a large-scale sequence analysis (100 virus clones) has been carried out over 25 patients of both subtypes in two regions of the HCV genome: one comprising hypervariable region 1 and another including the interferon sensitivity-determining region. Neither polymorphism analysis nor molecular variance analysis (attending to intra- and intersubtype differences, age, sex and previous history of antiviral treatment) was able to show any particular difference between subtypes that might account for their different prevalence. Only the demographic history of the populations carrying both subtypes and analysis of molecular variance (AMOVA) for risk practice suggested that the route of transmission may be the most important factor to explain the observed difference.

Analysis of Variance↗