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At least 649 records · Page 36Linked to original sources
PubMed1996

MIXOR: a computer program for mixed-effects ordinal regression analysis.

MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models. These models can be used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within clusters are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from clustering of the data. Similarly, for longitudinal data, the mixed-effects approach can allow for individual-varying intercepts and slopes across time, and can estimate the degree to which these time-related effects vary in the population of individuals. MIXOR uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution. For the scoring solution, the Cholesky factor of the random-effects variance-covariance matrix is estimated, along with the effects of model covariates. Examples illustrating usage and features of MIXOR are provided.

Antipsychotic Agents↗
PubMed2003

A quantitative structure-antifungal activity relationship study of oxygenated aromatic essential oil compounds using data structuring and PLS regression analysis.

Twenty two oxygenated aromatic essential oil compounds were chosen for the study of the antifungal activity against two wood-decaying fungi, the white-rot Trametes versicolor, which mainly metabolizes lignin, and the brown-rot Coniophoha puteana, which digests cellulose in plant cell walls. Minimal inhibitory concentrations (MICs) were determined by the agar dilution method, using dimethyl sulfoxide (DMSO) as the solvent for the selected compounds and potato-dextrose agar (PDA) as the growth medium for both fungi. The MICs were then used to generate a tree structure, which represents the structuring of the essential oil compounds by the nature and position of the substituents in their aromatic rings, and as dependent variables (log(1/MIC)) in the QSAR analysis. Data structuring proved that a relationship between the molecular structures of the essential oil compounds and their antifungal activity exists, and the hypotheses derived therefrom were complemented by performing a QSAR analysis using the partial least squares (PLS) method. Statistically significant PLS models were obtained with the 1-octanol-water partition coefficient (C log P), the energy of the highest occupied molecular orbital (E(HOMO)), and the number of hydrogen-bond donor atoms in the molecules of the compounds studied (Donor) for T. versicolor and with C log P and the fractional negative surface area (FNSA1) for C. puteana.

Aldehydes↗
PubMed1995

Percentile regression analysis of correlated antibody responses.

Antibody responses following vaccination usually are analysed by comparing geometric mean concentrations across levels of relevant covariates and by comparing the proportions of vaccinees responding. In the regression setting, the analyses are done on log-transformed concentrations, estimating geometric mean responses conditional on a vector of covariates. More detailed analyses examining the relationship of covariates to different parts of the response distribution may be performed through the application of asymmetric least squares estimation of regression percentiles. We present a method for accounting for correlation in percentile regression analyses of longitudinal antibody response data. We illustrate the procedures with measles antibody response data from Haitian children who participated in a randomized trial of high titre vaccines. The strongest dose and strain effects were seen in the low end of the antibody concentration distributions.

Age Factors↗
PubMed2005

Regression analysis of longitudinal binary data with time-dependent environmental covariates: bias and efficiency.

Generalized estimating equations (Liang and Zeger, 1986) is a widely used, moment-based procedure to estimate marginal regression parameters. However, a subtle and often overlooked point is that valid inference requires the mean for the response at time t to be expressed properly as a function of the complete past, present, and future values of any time-varying covariate. For example, with environmental exposures it may be necessary to express the response as a function of multiple lagged values of the covariate series. Despite the fact that multiple lagged covariates may be predictive of outcomes, researchers often focus interest on parameters in a 'cross-sectional' model, where the response is expressed as a function of a single lag in the covariate series. Cross-sectional models yield parameters with simple interpretations and avoid issues of collinearity associated with multiple lagged values of a covariate. Pepe and Anderson (1994), showed that parameter estimates for time-varying covariates may be biased unless the mean, given all past, present, and future covariate values, is equal to the cross-sectional mean or unless independence estimating equations are used. Although working independence avoids potential bias, many authors have shown that a poor choice for the response correlation model can lead to highly inefficient parameter estimates. The purpose of this paper is to study the bias-efficiency trade-off associated with working correlation choices for application with binary response data. We investigate data characteristics or design features (e.g. cluster size, overall response association, functional form of the response association, covariate distribution, and others) that influence the small and large sample characteristics of parameter estimates obtained from several different weighting schemes or equivalently 'working' covariance models. We find that the impact of covariance model choice depends highly on the specific structure of the data features, and that key aspects should be examined before choosing a weighting scheme.

Air Pollutants↗
PubMed1996

MIXREG: a computer program for mixed-effects regression analysis with autocorrelated errors.

MIXREG is a program that provides estimates for a mixed-effects regression model (MRM) for normally-distributed response data including autocorrelated errors. This model can be used for analysis of unbalanced longitudinal data, where individuals may be measured at a different number of timepoints, or even at different timepoints. Autocorrelated errors of a general form or following an AR(1), MA(1), or ARMA(1,1) form are allowable. This model can also be used for analysis of clustered data, where the mixed-effects model assumes data within clusters are dependent. The degree of dependency is estimated jointly with estimates of the usual model parameters, thus adjusting for clustering. MIXREG uses maximum marginal likelihood estimation, utilizing both the EM algorithm and a Fisher-scoring solution. For the scoring solution, the covariance matrix of the random effects is expressed in its Gaussian decomposition, and the diagonal matrix reparameterized using the exponential transformation. Estimation of the individual random effects is accomplished using an empirical Bayes approach. Examples illustrating usage and features of MIXREG are provided.

Adolescent↗
PubMed1994

Influence of nonlinearities on estimates of respiratory mechanics using multilinear regression analysis.

To investigate the influence of nonlinearities on estimates of respiratory mechanics, differing patterns of mechanical ventilation patterns were analyzed from 8 puppies and 14 children. Respiratory mechanics were calculated using multiple linear regression to fit a linear single-compartment model, a volume-dependent single-compartment model (VDSCM), and a flow-dependent single-compartment model. The ratio of the compliance of the last 20% of the dynamic volume-pressure (V-P) curve to the total compliance (C20/C) and the contribution of a volume-dependent elastance to total elastance [%E2 = E2 (VT)/[(E1 + E2)VT], where E1 + E2 is total elastance, E2 is the volume-dependent component, and VT is tidal volume] were used as the indexes of over-distension. By positioning the dynamic loops on the static V-P curves, ventilation patterns were classified as overdistended or nonoverdistended. In the overdistended group, the C20/C was significantly lower (0.71 +/- 0.10 vs. 0.92 +/- 0.16; P < 0.0001) and %E2 was significantly higher (43.4 +/- 15.0 vs. 0.51 +/- 18.02%, P < 0.0001) than in the nonoverdistended group. The mode of ventilation (pressure controlled vs. volume controlled) and the resistive pressures that resulted in widening of the dynamic V-P loop were found to alter C20/C but not %E2. When the respiratory system was overdistended, i.e., ventilated up to the flattened portion of the V-P curve, the VDSCM gave more accurate estimates of respiratory mechanisms. Furthermore, %E2 calculated from VDSCM is a useful parameter for estimating respiratory system overdistension that is not affected by resistive pressures.

Animals↗
PubMed1987

[Regression analysis in the study of dynamics of 5-nucleotidase activity in mouse peritoneal exudate macrophages].

The time course of the activity of 5-nucleotidase of peritoneal exudate macrophages (PEM) was studied on mice. Regression models of in time changes in the activity of PEM 5-nucleotidase after intraperitoneal administration of 2 immunostimulators: salmosan and blastolysine were constructed by the results of the study. Within the experimentally chosen periods of the study the time changes in the enzyme activity after administration of either salmosan or blastolysine could be adequately approximated by linear regression. The models enabled predicting the enzyme activity levels at various moments within wide ranges after administration of the immunostimulators. Moreover, it is recommended that the regression models of changes in 5-nucleotidase activity after intraperitoneal administration of the immunostimulators be used for quantitative comparison of their efficacy.

5'-Nucleotidase↗