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PubMed2004

Assessment of analysis-of-variance-based methods to quantify the random variations of observers in medical imaging measurements: guidelines to the investigator.

The random variations of observers in medical imaging measurements negatively affect the outcome of cancer treatment, and should be taken into account during treatment by the application of safety margins that are derived from estimates of the random variations. Analysis-of-variance- (ANOVA-) based methods are the most preferable techniques to assess the true individual random variations of observers, but the number of observers and the number of cases must be taken into account to achieve meaningful results. Our aim in this study is twofold. First, to evaluate three representative ANOVA-based methods for typical numbers of observers and typical numbers of cases. Second, to establish guidelines to the investigator to determine which method, how many observers, and which number of cases are required to obtain the a priori chosen performance. The ANOVA-based methods evaluated in this study are an established technique (pairwise differences method: PWD), a new approach providing additional statistics (residuals method: RES), and a generic technique that uses restricted maximum likelihood (REML) estimation. Monte Carlo simulations were performed to assess the performance of the ANOVA-based methods, which is expressed by their accuracy (closeness of the estimates to the truth), their precision (standard error of the estimates), and the reliability of their statistical test for the significance of a difference in the random variation of an observer between two groups of cases. The highest accuracy is achieved using REML estimation, but for datasets of at least 50 cases or arrangements with 6 or more observers, the differences between the methods are negligible, with deviations from the truth well below +/-3%. For datasets up to 100 cases, it is most beneficial to increase the number of cases to improve the precision of the estimated random variations, whereas for datasets over 100 cases, an improvement in precision is most efficiently achieved by increasing the number of observers. For datasets of at least 50 cases, the standard error ranges between 30% or less with 3 observers down to 10% or less with 8 observers, and the differences in precision between the methods are negligible. The F test (PWD) is very anticonservative and should not be used, while the t test (RES) is reliable for datasets of at least 2 x 50 cases evaluated by 4 or more observers. The likelihood-ratio-test (REML estimation) consistently indicates the significance of a difference in the random variation of an observer between two groups of cases, regardless of the number of cases, and regardless of the number of observers. If a statistical package to perform REML estimation is available, and the investigator feels confident using it, this is the preferred method for studies that involve less than 50 cases evaluated by less than 6 observers. Otherwise, the RES method is an excellent alternative, because of its straightforward implementation, its completeness with respect to the provided statistics, and its overall sufficient accuracy, precision, and reliability of the provided statistical test. If neither the RES method nor REML estimation can provide sufficient performance, either more observers or more cases must be included.

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PubMed1996

Mean power spectra from pharmaco-electrocorticographic studies: relative baseline correction and log transformation for a proper analysis of variance to assess drug effects.

The effects of drugs on electrocorticographic activity (ECoG) of the rat are studied in a routine screen. ECoG is recorded for 6-min periods before drug/vehicle administration and starting at 20 and 45 min thereafter. For each period, a mean power spectrum is calculated. Drugs are tested in 25 rats according to a 5 x 5 Latin square design and effects are assessed with analysis of variance. The validity of this assessment depends on assumptions on the chosen statistical model and the distribution of the data. In this study we consider the relative and absolute baseline corrected data. The assumptions appear to be better fulfilled if together with a relative baseline correction a logarithmic transformation is applied to the data.

Analysis of Variance↗
PubMed1999

Detecting activations in event-related fMRI using analysis of variance.

The most common design of a functional MRI (fMRI) experiment is a block design. The use of rapid imaging, however, and carefully designed paradigms makes the separation of cognitive events possible. Such experiments make use of event-related paradigms, in which a task involving several cognitive processes is repeated. In analyzing data from such experiments, existing methods often prove inadequate, because the prediction of the exact shape or timing of the time course is difficult. Here we present an analysis of variance (ANOVA) method for analyzing fMRI data that does not require any assumptions about the shape of the activation time course. Consequently, this method can simultaneously detect brain areas showing a variety of stimulus-locked time courses in the same experiment. The utility of this technique is demonstrated by the analysis of data from two event-related paradigms in which regions of activation are detected that correspond to a variety of distinct neural processes, yielding significantly different temporal signal changes. Magn Reson Med 42:1117-1122, 1999.

Analysis of Variance↗
PubMed1983

[Relevant prognostic results in stomach cancer (analysis of variance and discriminance with clinical, morphologic and immunologic features)].

In 47 patients with an endoscopic-bioptic reliable gastric cancer two prognostic groups (good/poor) were set up by the help of clinical TNM-determination (gastroscopy, laparoscopy, operation), histology (bioptate and resectate) and long-term observation. In order to determine their prognostic significance 37 clinical, morphological and immunological parameters were recorded and analysed by the help of a multivariate analysis of variance and discriminance. The TNM-stage, erythrocyte sedimentation rate, the acid mucopolysaccharides in the tumour, circulating, complement binding immuncomplexes, B-cells, IgM, C3 and C4, IgE, gastritic degree and the infiltration of the tumour by neutrophile granulocytes and lymphocytes prove to be prognostic-discriminatoric significant. The parameters allowed a retrogradually-mathematically reliable classification of the patients into one of the both prognostic groups. The remaining features (age, sex, duration of the case history, Rohrer-index, tumour superficial spread, histological differentiation, neutral mucopolysaccharides, gastric activity, plasma cells and macrophages as stroma cells, IgG, IgA, auto-antibodies, dysproteinaemies, PEG-precipitate, T-cells, LAI-test, haemoglobin, total leucocyte count and total protein) prove to be redundant, i.e. they are unnecessary for calculation of the prognosis.

Analysis of Variance↗
PubMed1982

Computer image analysis of variance between human chromosome replication sequences and G-bands.

A computer image analysis system was applied to the quantitative study of chromosomal early- and late-replication patterns from the leukocytes of several normal human donors, and these patterns were compared with the chromosomal G-banding patterns. The first and last few hours of replication were discriminated by selective bromodeoxyuridine vs. thymidine incorporation in DNA and a Hoechst-blacklight-Giemsa stain technique. Image analysis with Tufts Piquant system involved automatic determination of chromosome boundaries, centromeres and telomeres, linear chromatid axes, chromatid density measurements along each axis, and comparative length normalized density profiles for each chromatid and the chromosome. Consistent complementary early- and late-replication patterns were determined for autosomes 1-6 and the X chromosomes. Limited intracellular or interindividual variability occurred in the intensity of a few active replication peaks but not in their location. However, there were very distinct regions of noncorrespondence between the late-replication patterns and the G-band patterns, in contrast with previous observations, although many similarities were also evident. These differences are interpreted with reference to a general model of replication sequence control of cell differentiation.

Analysis of Variance↗
PubMed1991

Data analysis and sample size issues in evaluations of community-based health promotion and disease prevention programs: a mixed-model analysis of variance approach.

The growing interest in community-based approaches to health promotion and disease prevention (HP/DP) has been accompanied by a growing need to evaluate the effectiveness of such programs. Special issues that arise in these evaluation studies include (1) entire communities are assigned to intervention and control groups, (2) only a small number of communities can usually be studied, (3) the time course of changes in behavior and other outcomes is often of interest, and (4) surveys to measure such changes over time can be conducted with either repeated cross-sectional samples or with longitudinal samples. This paper shows how these issues can be addressed under a mixed-model analysis of variance approach. This approach serves to unify several ideas in the literature on evaluation of community studies, including use of time-series regression and the question of whether the individual or the community should be the unit of analysis. We also describe how the method can be used to estimate sample size requirements, statistical power, or minimum detectable program effect.

Analysis of Variance↗
PubMedDate not supplied

High hydrostatic pressure inactivation of Salmonella typhimurium: effects of pressure, duration, pH and temperature studied by analysis of variance.

High hydrostatic pressure treatments are regarded as possible alternative methods for food preservation. One of the primary considerations for industrial applications is the ability of these methods to eradicate pathogenic microorganisms. This study subjected S. typhimurium suspensions, first in a phosphate buffer (pH 7.0) and then in a citrate phosphate buffer (pH 5.6), to high hydrostatic pressure treatments relative to the following variables: pressure (200-400 MPa), duration (3, 10 and 20 min), temperature (4, 20 and 40 degrees C) and the pH of the suspension medium (5.6 and 7.0). An optimal design of 40 runs was obtained using the Fedorov algorithm, and responses were studied by analysis of variance in terms of cell survival on plate count agar. Efficiency was determined by Log10 comparisons of the numbers of live cells before and after treatment. A statistically significant relationship was found between the four variables considered (pressure, pH, duration and temperature), their interactions (duration x pressure, pH x temperature, pH x pressure) and the inactivation of S. typhimurium. R-squared statistical analysis indicated that the linear model used accounted for more than 98% of the variability in the inactivation of S. typhimurium.

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PubMed1992

[Morphometric-stereological study of hippocampal long term potentiation with special consideration of the analysis of variance with two-way hierarchical classification].

Regarding the hippocampal formation and especially the external two thirds of it's dentate molecular layer a lot of possible morphological changes after long-term potentiation (LTP) have been described in literature. The present morphometric-stereological study of vesicles in axo-spino-dendritic synapses of the inner third of the molecular layer was done under the aspect of heterosynaptic influences following LTP. Because of the hierarchical link of the three analytic levels (test-group, animal, synapse), for statistical interpretation we used the analysis of variance with two-way hierarchical classification. Between the 3 groups (passive control, active control, LTP-group) we found no significant differences. Because of the great differences between the vesicles even within a single synapse we subsequently investigated the middle third of the molecular layer, i.e. the terminal area of the stimulated perforant path. No differences between the three groups we found here either. There was no confirmation for the expected greater homogenization of the synapses based on the uniform input. As a result of this study pure morphological studies without selective staining of specific population of synapses are considered inadvisable. Only with the help of selective staining in the area of the synapses possible differences between the groups may be found.

Analysis of Variance↗
PubMed1997

Learning and understanding the Kruskal-Wallis one-way analysis-of-variance-by-ranks test for differences among three or more independent groups.

When several treatment methods are available for the same problem, many clinicians are faced with the task of deciding which treatment to use. Many clinicians may have conducted informal "mini-experiments" on their own to determine which treatment is best suited for the problem. These results are usually not documented or reported in a formal manner because many clinicians feel that they are "statistically challenged." Another reason may be because clinicians do not feel they have controlled enough test conditions to warrant analysis. In this update, a statistic is described that does not involve complicated statistical assumptions, making it a simple and easy-to-use statistical method. This update examines the use of two statistics and does not deal with other issues that could affect clinical research such as issues affecting credibility. For readers who want a more in-depth examination of this topic, references have been provided. The Kruskal-Wallis one-way analysis-of-variance-by-ranks test (or H test) is used to determine whether three or more independent groups are the same or different on some variable of interest when an ordinal level of data or an interval or ratio level of data is available. A hypothetical example will be presented to explain when and how to use this statistic, how to interpret results using the statistic, the advantages and disadvantages of the statistic, and what to look for in a written report. This hypothetical example will involve the use of ratio data to demonstrate how to choose between using the nonparametric H test and the more powerful parametric F test.

Analysis of Variance↗
PubMed2003

Effects of mRNA amplification on gene expression ratios in cDNA experiments estimated by analysis of variance.

BACKGROUND: A limiting factor of cDNA microarray technology is the need for a substantial amount of RNA per labeling reaction. Thus, 20-200 micro-grams total RNA or 0.5-2 micro-grams poly (A) RNA is typically required for monitoring gene expression. In addition, gene expression profiles from large, heterogeneous cell populations provide complex patterns from which biological data for the target cells may be difficult to extract. In this study, we chose to investigate a widely used mRNA amplification protocol that allows gene expression studies to be performed on samples with limited starting material. We present a quantitative study of the variation and noise present in our data set obtained from experiments with either amplified or non-amplified material. RESULTS: Using analysis of variance (ANOVA) and multiple hypothesis testing, we estimated the impact of amplification on the preservation of gene expression ratios. Both methods showed that the gene expression ratios were not completely preserved between amplified and non-amplified material. We also compared the expression ratios between the two cell lines for the amplified material with expression ratios between the two cell lines for the non-amplified material for each gene. With the aid of multiple t-testing with a false discovery rate of 5%, we found that 10% of the genes investigated showed significantly different expression ratios. CONCLUSION: Although the ratios were not fully preserved, amplification may prove to be extremely useful with respect to characterizing low expressing genes.

Analysis of Variance↗
PubMedDate not supplied

Applicational possibilities of linear and non-linear (polynomial) regressions and analysis of variance: kinetics of chloramphenicol.

The dependance of the content and microbiological activity of Chloramphenicol (active substance) at dissolution on time and on pH, as well as that of the content and microbiological activity of Chloramphenicol (250 mg capsules) at release, on time, in in-vitro conditions, was determined using linear and non-linear (polynomial and generalized dilution) regressions. Based on the square error value, the dependance of the content and microbiological activity of Chloramphenicol (active substance) at dissolution on time and different pH values, as well as the dependance of the content and microbiological activity of Chloramphenicol (capsules) at release on time were best described by polynomial function. The comparison of the content and microbiological activity of Chloramphenicol (active substance) at dissolution at different pH values, as well as of Chloramphenicol (capsules) at release showed the significant correlation between these parameters (r = 0.999, P << 0.001). The comparision of the content, on one hand, and microbiological activity of Chloramphenicol (active substance), on the other, at dissolution at different pH values, as a function of time, was done using a modified method of one-way analysis of variance for linear regression comparisons. Based on the value of Fischer's coefficient (F), there is a statistically very significant difference between the contents and between the microbiological activities of chloramphenicol (active substance) at dissolution and different pH as a function of time (P << 0.005).

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