Numerical analysis of kinetic data: collapsed regression analysis for first order reactions.
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Enzymes↗
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Results for “Regression Analysis”
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[Multivariate analysis in computer diagnosis. 4. Regression analysis and some remarks on its application].
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Diagnosis, Computer-Assisted↗
Advantages of examining multicollinearities in regression analysis.
In this paper a regression analysis is performed with data on spinal cord injuries in order to demonstrate the benefits of determining which, if any, multicollinearities are present in prediction data. Existing multicollinearities are shown to be useful both in determining characteristics of the sampled population as well as explaining possible erratic behavior of variable selection procedures. Latent root regression is performed on the data to illustrate one method of using biased regression techniques to incorporate knowledge of multicollinearities in developing prediction equations.
Humans↗
The significance for breeding of linear regression analysis of genotype-environment interactions.
Methods of regression analysis of genotype-environment interaction are considered in relation to existing theory dealing with the relative efficiencies of selection for general or specific adaptation to the environment, and the choice of environments for assessment. The two alternative models is involving regression on to environmental effects (model 2) or genotypic effects (model 3) are equivalent when regression lines are concurrent, but are shown to be mutually exclusive when concurrence is absent...
Crosses, Genetic↗
Regression analysis in interlaboratory surveys: a case study with cholesterol and triglycerides.
1. A new interlaboratory survey design, that uses regression analysis to compare results from each laboratory with target values, was tested using cholesterol and triglyceride analyses. The fifty New Zealand laboratories involved showed considerable interlaboratory variation (CV = 8% to 27% for cholesterol, 13% to 113% for triglycerides), 30% and 40% of which was associated with systematic differences between laboratories. 2. End-of-period summaries using regression analysis confirmed the presence of systematic errors. These were either simple types caused apparently by incorrect standardisation (regression slope, B not equal to 1.0) or inappropriate blank correction (intercept, A not equal to zero) or complex types presumably due to nonlinearity or nonspecificity. Graphical display of results from each laboratory aided fault diagnosis and allowed the detection of between-run standardisation differences. 3. Method comparison studies were made: the only highly significant result being lower precision achieved by enzymatic cholesterol methods compared with other colorimetric methods.
Blood Chemical Analysis↗
Estimation of age from the pubic symphysis by means of multiple regression analysis.
This study has been carried out to assess the age from the pubic symphysial surface employing a multiple regression analysis and a quantification theory model I analysis. Using partial regression coefficients and/or normalized scores obtained from the analyses, ages of skeletal remains can be quantitatively estimated with a fairly high reliability. The use of this method is, however, limited to the samples between 18 and 38 years of age, because age changes in the symphysial surface show large variations after about 40 years. The reliability of this method was also examined.
Adolescent↗
Error structure of enzyme kinetic experiments. Implications for weighting in regression analysis of experimental data.
Knowledge of the error structure of a given set of experimental data is a necessary prerequisite for incisive analysis and for discrimination between alternative mathematical models of the data set. A reaction system consisting of glutathione S-transferase A (glutathione S-aryltransferase), glutathione, and 3,4-dichloro-1-nitrobenzene was investigated under steady-state conditions. It was found that the experimental error increased with initial velocity, v, and that the variance (estimated by replicates) could be described by a polynomial in v Var (v) = K0 + K1 - v + K2 - v2 or by a power function Var (v) = K0 + K1 - vK2. These equations were good approximations irrespective of whether different v values were generated by changing substrate or enzyme concentrations. The selection of these models was based mainly on experiments involving varying enzyme concentration, which, unlike v, is not considered a stochastic variable. Different models of the variance, expressed as functions of enzyme concentration, were examined by regression analysis, and the models could then be transformed to functions in which velocity is substituted for enzyme concentration owing to the proportionality between these variables. Thus, neither the absolute nor the relative error was independent of velocity, a result previously obtained for glutathione reductase in this laboratory [BioSystems 7, 101-119 (1975)]. If the experimental errors or velocities were standardized by division with their corresponding mean velocity value they showed a normal (Gaussian) distribution provided that the coefficient of variation was approximately constant for the data considered. Furthermore, it was established that the errors in the independent variables (enzyme and substrate concentrations) were small in comparison with the error in the velocity determinations. For weighting in regression analysis the inverted value of the local variance in each experimental point should be used. It was found that the assumption of proportionality between variance and valpha (where alpha is an empirically determined exponent) was a good approximation for the weighting. The value of alpha was 1.6 in the present case. The weight function was tested in the fitting of a rate equation to a kinetic-data set involving variable substrate concentrations. Recommendations are given regarding the establishment of the error structure in a general case and its application in regression analysis.
Analysis of Variance↗
[Analysis of multiple variance in computer diagnosis. (5) Regression analysis].
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Adult↗
Composition-on-composition regression analysis for multi-omics integration of metagenomic data.
MOTIVATION: Compositional data are frequently encountered in many disciplines, such as in next-generation sequencing experiments widely used in biomedical studies. Regression analysis with compositional data as either responses or predictors has been well studied. However, when both responses and predictors are compositional, the inventory of analysis tools is surprisingly limited, especially in the high-dimensional setting. Among the few existing methods, most of them rely on a log-ratio transformation to move compositional data from the simplex to real numbers. Yet, a serious weakness of these methods is their failure to handle the substantial fraction of zeroes observed in data collected from next-generation sequencing experiments. RESULTS: To investigate associations between two high-dimensional multi-omics compositions, we propose a composition-on-composition (COC) regression analysis method which does not require log-ratio transformations and hence can handle zeroes in the data. To account for high dimensionality, we estimate regression coefficients using a penalized estimation equation approach. Finally, inference procedures for COC regression are also proposed. Superior performance of COC is demonstrated through both comprehensive numerical simulations and case studies. AVAILABILITY AND IMPLEMENTATION: Source R codes to implement COC method is available at https://github.com/nrios4/COC.
Regression Analysis↗
[Use of regression analysis in the quantitative evaluation of the activity of biological preparations].
Use of regression analysis in the assessment of the activity of biological preparations under experimental conditions permitted not only to assess the quantitative effect (ED50) more strictly, but also to find other parameters of importance for the results of comparison, for example with the standard, i.e. in standardization. To these belong regression coefficient, parallelism of regressions, and the relative potency. By the presence of a parallelism one can judge the similarity between the activity mechanism of the active principle of the preparations being compared. Relative potency characterizes the activity of the preparation in the relative values in comparison with the standard, with a statistical evaluation of this value with the aid of the confidence interval. The authors suggest a program for Mir-2 computer facilitating the calculations in using the analystical method which is more objective than the graphic method of assessment of the linear dosage-response curve.
Animals↗
Regression analysis of the relationship between physical properties and the in vitro inhibition of monoamine oxidase by propynylamines.
Regression analysis of the potency of inhibition of monoamine oxidase by 47 propynylamines revealed that there are three determinants of inhibitory potency: (1) the smallest substituent on the nitrogen must be methyl or hydrogen in order for any activity to be observed; (2) potency is parabolically related to pKa-the optimum pKa is 6.2; and (3) ortho-substituted benzylamine analogs are ten times more potent than predicted on the basis of pKa values. The optimum pKa cannot be explained by differences in fraction ionized but rather in terms of the multistep sequence whereby these compounds inhibit MAO. A very slight positive effect of hydrophobicity on potency was found. The potency of several analogs not included in the original analysis was predicted.
Alkynes↗
Assessment of biological age by multiple regression analysis.
In order to generate a reference value of aging, estimation of chronological age of healthy subjects was made by multiple regression analysis. It was shown that the estimated ages of hypertensive subjects by the formula used were significantly higher than their chronological ages. It was further observed that parameters representing various ability of movements might yield an alternative regression formula and that the accuracy in age estimation was improved with application of a second-order regression formula.
Accommodation, Ocular↗
The prediction of glaucoma from ocular biometric data. Part 1. An application of multiple regression analysis.
Two sets of multiple regression equations (prediction systems) were derived from the analysis of ocular biometric data obtained from glaucoma patients (16 open angle; 16 angle-closure) and 75 normal subjects. Discriminant scores were established for both sets of equations which minimised the number of false negatives. One set, the 'Glaucoma Equations' was applied to the data to segregate the glaucoma from the normal subjects. The other prediction system, the 'Classification Equations,' was then applied to the group defined as glaucomatous to discriminate between patients in the angle-closure and open angle categories. The performance of these equations, obtained by comparing the predicted and actual classifications for this sample, was such that between 9 and 12% of false positives and 0 and 3% false negatives were found on the 'Glaucoma Equations' and between 6 and 12% of false positives with no false negatives on the 'Classification Equations.'
Age Factors↗
Tutorial: application of regression analysis concepts to retrospective research in speech pathology.
The underlying logic of retrospective research in speech pathology is examined critically. This research model focuses on differences among discrete groups. Regression analysis concepts are suggested as a tool to evaluate the practical or theoretical importance of statistically significant differences obtained in retrospective research. This type of analysis focuses on the degree to which a criterion measure can be predicted from knowledge of group classification, rather than on differences among means.
Adult↗
Factors influencing blood concentrations of chlordiazepoxide: a use of multiple regression analysis.
Three groups of male and female subjects aged 24-74 years received 25, 100, or 200 mg of chlordiazepoxide hydrochloride by mouth as a single dose or as two divided doses. The relation of plasma or whole blood concentrations for chlordiazepoxide (CDX) and its metabolite, desmethylchlordiazepoxide (DMCDX), to time since the last dose, weight, age, and sex were determined by simple and multiple regression analyses. Both CDX and DMCDX levels were negatively correlated with weight. Concentrations of CDX decreased, while those of DMCDX increased, with the time since the last dose. Lower levels of both drugs were associated with female sex, and lower levels of DMCDX were noted with increasing age. In the largest sample group, age and weight were more important variables than sex in accounting for CDX and DMCDX. Sex was of significance, and more important than time or age in explaining the variance of CDX in one series of observations. Multiple regression analysis is a useful approach to assessing interrelated factors influencing blood levels of drugs, especially when combined with a consideration of the interactive components of variance. Age and sex, in addition to weight and time, may be important factors that deserve further attention.
Adult↗
Morale of the urban aged: a regression analysis by race.
This research examined the degree to which previous factors shown to be related to morale were isomorphic for aged whites and aged blacks. The data consisted of a random sample of low income aged blacks and whites in Philadelphia and were collected by the late Donald P. Kent as part of the Aged Services Porject and consisted of 722 black elderly and 214 white elderly, all of whom were 65 years of age or older. The results of a regression analysis indicated that for blacks the only two significant predictors of morale were health and participation in solitary activities. For whites, in addition to health and solitary activities, interaction with family and sex were also found to be significant. It was concluded that for these data the predictors are not isomorphic between races.
Black or African American↗
Discrimination between descriptive models of L-glutamate uptake by the retina using non-linear regression analysis.
1. The uptake of labelled L-glutamate by the isolated rat retina was measured over a large range of external concentrations (1 micron to 1 mM). 2. The results obtained from measurements of the initial velocity of L-glutamate uptake at different concentrations did not follow simple hyperbolic kinetics. 3. The error structure of replicate velocity measurements was examined and found to be normally distributed and heteroscedastic. 4. Descriptive models were fitted directly to data, weighted by the invariance, using non-linear regression analysis. 5. The most suitable suitable descriptive model consisted of a saturable hyperbola (Vm = 285 n-mole.(g wet wt.)-1.min-1, Km = 252 micron) and a linear term (b = 0.45 min-1).
Animals↗
Distribution-free regression analysis of grouped survival data.
Methods based on regression models for logarithmic hazard functions, Cox models, are given for analysis of grouped and censored survival data. By making an approximation it is possible to obtain explicitly a maximum likelihood function involving only the regression parameters. This likelihood function is a convenient analog to Cox's partial likelihood for ungrouped data. The method is applied to data from a toxicological experiment.
Animals↗