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PubMed1992

Analysis of variance of parameter estimates: F tests and t tests.

The problem of comparing and pooling experimentally independent estimates of a parameter such as a Michaelis constant (K) has been treated as a simple analysis of variance of "within" and "between" set deviations from the fitted variable (v). As applied to assessing the reproducibility of multiple estimates of the same K, this is identical to the procedure of Duggleby (Anal. Biochem. 189, 84-87, 1990). However, the theory developed here shows that applying Duggleby's procedure to the comparison of two experiments (each consisting of multiple data sets) depends critically on the assumption of equal errors within and between the individual sets, i.e., Fvb vw = s2wv/s2bv is close to 1. Application of the method when this is not the case will underestimate the common error (s2rv), overestimate its associated degrees of freedom (vr = vb+vw), and may suggest apparently significant differences where there are none. The theory also shows that this situation is an instance of the Fisher-Behrens problem and shows how Welch's solution can be applied. This gives the between set error s2bv as the corrected estimate of the common error and the corrected degrees of freedom as a simple function of vb, vw, and Fvb vw. When the nine prephenate dehydratase data sets which originally showed three apparently significant differences were reanalyzed in this way, all the variations in K were found to be within the range of the experimental error.

Analysis of Variance↗
PubMed2005

Efficiency control in large-scale genotyping using analysis of variance.

The efficiency of the genotyping process is determined by many simultaneous factors. In actual genotyping, a production run is often preceded by small-scale experiments to find optimal conditions. We propose to use statistical analysis of production run data as well, to gain insight into factors important for the outcome of genotyping. As an example, we show that analysis of variance (ANOVA) applied to the first-pass results of a genetic study reveals important determinants of genotyping success. The largest factor limiting genotyping appeared to be interindividual variation among DNA samples, explaining 20% of the variance, and a smaller reaction volume, sizing failure, and differences among markers all explained approximately 10%. Other potentially important factors, such as sample position within the plate and reusing electrophoresis matrix, appeared to be of minor influence. About 55% of the total variance could be explained by systematic factors. These results show that ANOVA can provide valuable feedback to improve genotyping efficiency. We propose to adjust genotype production runs using principles of experimental design in order to maximize genotyping efficiency at little additional cost.

Analysis of Variance↗
PubMedDate not supplied

Robust alternatives to traditional analysis of variance: Welch W*, James J I*, James J II*, Brown-Forsythe BF*.

Contaminated data sets commonly appear in biomedical sciences. Traditional statistical analyses are based on a 'normality' assumption and homogeneity of variances assumption. If either or both assumptions are violated, traditional statistical procedures may give an inflated type I error rate and are then not robust. Robust alternatives to traditional one-way analysis of variance have better power curves and protect against inflated type I error rates.

Analysis of Variance↗
PubMed1992

[Estimation of genetic parameters by using analysis of variance within unit].

In the estimation of heritability, various non-genetic factors must be excluded from the variance of sire or dam. The methods of sib correlation within unit, suggested by professor Sheng Zhilian is commonly used in China. This paper will provide evidence to verify this method theoretically and discuss the application of the method to systematic classification. In addition, in this paper the author suggest some other methods to estimate heritability by using analysis of variance within unit when the blood relationship exists between sire and dam. These methods not only make the estimation procedure of hertability simple but also have the same function of analyzing variance with multiple factors.

Analysis of Variance↗
PubMed2001

Variance components analysis for genetic linkage of time to onset for disease.

We compared two variance components methods for detecting genes that influence time to onset for a complex disease using simulated data. We first divided the extended families into nuclear families. The first method fitted variance components to the martingale residuals, which were obtained from first fitting a proportional hazards model to the time to onset data for the trait, allowing for the quantitative traits Q1-Q5, sex, age, and the environmental factor. The second method treated time to onset among the affected individuals as a quantitative trait adjusting for the same factors as in the first method. Power of these analyses were similar for either approach. However, we found an excess of false-positive results when fitting the martingale residual model or the affected-only model to identify genetic factors linked to chromosome 6. Applying a power transformation to the martingale residuals decreased the type I error rate and increased the power of tests for genetic linkage. We also found that robust variance correction lead to test with a slightly lower type I error rate, perhaps because the robust variance correction adjusts for the fact that we did not specifically model the effects of the mitochondrial factor in our analysis.

Analysis of Variance↗
PubMed2006

A comparison of analysis of variance and correlation methods for investigating cognitive development with functional magnetic resonance imaging.

Statistical approaches used in functional magnetic resonance imaging (fMRI) to study cognitive development are varied and evolving. Two approaches have generally been used. These are between-group end-point analysis of variance (ANOVA) and age-related regression. Differences in these 2 approaches could produce different results when applied to a single data set. Event-related fMRI data from a group of typically developing participants (n = 95; age range = 7-35 years) performing controlled lexical processing tasks were analyzed using both methods. Results from the 2 approaches showed significant overlap, but also noteworthy differences. The results suggest that for regions showing age-related changes, correlation was relatively more sensitive to more linear changes whereas ANOVA was relatively more sensitive to less-linear changes. These findings suggest that full characterization of developmental dynamics will require converging methodologies.

Adolescent↗
PubMed1982

Analysis of variance in assessing registrations of natural head position.

Recordings of natural head position may be performed directly during radiographic registration or indirectly by a photographic transfer procedure. The aim of this investigation was to examine and quantify the different sources of error in one photographic method using an analysis of variance. The material consisted of 15 senior dental students between the ages of 24-34 years. Intra-observer results show that the error associated with transfer of the plumb line to the tracing and digital recording of the reference points is small in comparison with the tracing error and intra-individual postural variation as registered photographically. By relating the total analysis error to the inter-individual postural variation it was possible to perform a preliminary evaluation of the precision of the entire registration method prior to continued collection of raw data. In this case it was found that duplication of tracings and photographs was the most rational approach resulting in an analysis error equivalent to 9.5 percent of the estimated postural variation between individuals.

Adult↗
PubMed1981

A multitrait-multimethod analysis of variance of teachers' ratings of aggression, hyperactivity, and inattention.

The convergent and discriminant validities of three teacher rating scale measures of the traits of hyperactivity, aggression, and inattention were explored, using the multitrait-multimethod matrix approach of Campbell and Fiske (1959), as well as an analysis of variance procedure (Stanley, 1961). In the present study teachers rated children from their elementary school classrooms on the above traits. The results provided strong evidence for convergent validity. Data also indicated that these traits can be reliable differentiated by teachers, suggesting that research aimed at better understanding the unique contributions of hyperactivity, aggression, and inattention is warranted. The respective benefits of analyzing multitrait-multimethod matrices by employing the ANOVA procedure or by using the Campbell and Fiske (1959) criteria were discussed.

Aggression↗
PubMed1993

The misuse of analysis of variance to detect synergy in combination drug studies.

Drug combination studies often examine the possibility of synergy between drugs. Synergy is defined as an effect of a combination of drugs greater than that expected from the effects of the drugs given individually. One technique used by several investigators is the use of analysis of variance (ANOVA) to determine synergy. In this discussion, the argument is made that due to the pharmacology of drug combination studies the conditions necessary to support the use of ANOVA to detect synergy are typically not met. Therefore, the ANOVA technique is invalid for these drug combination studies.

Analysis of Variance↗