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PubMed2000

[Recognition of multiple information of human auricular points by linear model].

The three variables of the electric characteristic of human auricular points can be regarded as observed targets that reflect the physiologic or pathologic changes. Medical principle design follows the theory of the channels and collaterals and the viscera-state doctrine of traditional Chinese medicine and that of the pattern recognition. The linear integration model can be established by drawing the characteristic variables of the correlation groups of the auricular points and applying the multi-variable normalization. With this model, the recognition of the three variations of the auricular points can be of use for differential diagnosis. In this paper, the test to identify the cases of Upper-Digestive tract achieves good result. This research, based on the integration of modern science with traditional Chinese medicine, provides a method for building the specialist system of the auricular point diagnosis, and probably has some prospect in clinical application.

Acupuncture Points↗
PubMed1982

Effects of unequal pressure swings and different waveforms on distribution of ventilation: a non-linear model simulation.

In an attempt to understand the role of unequal pleural pressure swings and of different waveforms of pleural pressure variation in the distribution of ventilation during cyclic breathing, a mathematical model simulation was performed. The computer model which incorporates non-linear resistances and compliances as well as sinusoidal, square, and triangular waveforms of pleural pressure variations indicates that the distribution of ventilation is insensitive to the waveform of the pleural pressure. The distribution is also little changed by the depth of breathing (amplitude), but it is affected significantly by the pattern of different pressures over the regions of the model. For sinusoidal, triangular, and low amplitude square wave pleural pressures with equal amplitudes on both compartments, air was distributed preferentially to the lower compartment under the influence of the static pressure difference. With unequal amplitudes, more air flowed to the compartment experiencing the larger pressure swing. This was virtually independent of the waveform and of the amplitudes of the pleural pressure variation. Comparison of the present results with a constant flow model reveals that the overall distribution of tidal air during cyclic breathing is very different from the results obtained in constant rate inspiration experiments or in bolus distribution experiments. New experiments performed under cyclic breathing conditions are thus indicated.

Humans↗
PubMed1989

Determination of enzyme or binding constants using generalized linear models, with particular reference to Michaelis-Menten models.

The estimation of Michaelis-Menten pharmacokinetic parameters in patients with epilepsy receiving phenytoin continues to be a vexing problem. The various approximate methods suggested in the literature have serious shortcomings, primarily due to the role of the error term in the statistical model. In this report we present an accurate statistical approach using the Generalized Linear Interactive Modeling (GLIM) computer package developed by the Numerical Algorithms Group, Oxford, U.K. There are several advantages to this model: a meaningful error term can be maintained by means of a link function, the model can incorporate within-subject and between-subject variables, and additional potential explanatory variables can be added to the model. The method is applied to predicting serum phenytoin levels of pregnant women monitored at monthly intervals during pregnancy and for two to five months after pregnancy. Michaelis-Menten parameters are estimated for each women and compared.

Adult↗
PubMed1995

Calculation of biliary atresia prognostic index using a multivariate linear model.

The purpose of this study was to determine the relative value of liver function markers in predicting the magnitude of morbidity and to develop a quantitative estimate of the prognostic risk using a multivariate regression model in patients who have been operated on for biliary atresia. The study sample consisted of 37 patients who had undergone a Kasai portoenterostomy; the jaundice disappeared in 32 and persisted in five. A computer-based stepwise regression procedure produced the linear predictive models by the equation: biliary atresia prognostic index (BAPI) = 9.2 Cu:Zn + 1.0 ZTT + 3.2 TB - 0.05 ChE + 9.9 for infants under 1 year of age, and BAPI = 10.3 Cu:Zn + 0.8 ZTT + 0.03 GGTP - 0.12 ChE + 25.6 for children over 1 year of age. In validation of these models, the indexes fluctuated from -17 to 122, and the degree of morbidity increased linearly with the increase in BAPI. Postoperatively the patients were classified into four categories according to the dynamics of their postoperative course: A (BAPI < 25), successful cases that should not require liver transplantation (40.5%); B (25 < or = BAPI < or = 50), improved cases that have extended survival with their native liver (29.7%); C (50 < BAPI < or = 75), cases that improved in terms of disappearance of jaundice but ultimately will require liver transplantation (8.1%); and D (BAPI > 75), cases that require early referral for transplantation (21.6%). (The percentages indicate the distribution rate of patients at the time of final follow-up evaluation.) These models allow quantification of the risk of morbidity from progressive liver cirrhosis in the individual patient, permitting the clinician to consider whether such patients should be considered for liver transplantation.

Biliary Atresia↗
PubMed1998

A linear model method for rank measures of association from longitudinal studies with fixed conditions (visits) for data collection and more than two groups.

Several statistical methods are available for the analysis of responses with ordinal categories or continuous distributions for the respective visits in longitudinal studies. This paper discusses an alternative nonparametric strategy for studies with more than two groups through Mann-Whitney rank measures of association for all pairs of groups. The proposed method is based on U-statistic theory, and it applies a linear or linear logistic model to the Mann-Whitney estimators for the probabilities of better response for each group relative to each of the others. In addition, the ways of adjusting for covariables and managing stratification factors are explained. Analysis of parallel dose-response relationships for two treatments is illustrated for the proposed method with data from a multicenter study with repeated measurements. A nonparametric estimator for relative potency is provided from the method.

Algorithms↗
PubMed2004

Dimension reduction in the linear model for right-censored data: predicting the change of HIV-I RNA levels using clinical and protease gene mutation data.

With rapid development in the technology of measuring disease characteristics at molecular or genetic level, it is possible to collect a large amount of data on various potential predictors of the clinical outcome of interest in medical research. It is often of interest to effectively use the information on a large number of predictors to make prediction of the interested outcome. Various statistical tools were developed to overcome the difficulties caused by the high-dimensionality of the covariate space in the setting of a linear regression model. This paper focuses on the situation, where the interested outcomes are subjected to right censoring. We implemented the extended partial least squares method along with other commonly used approaches for analyzing the high-dimensional covariates to the ACTG333 data set. Especially, we compared the prediction performance of different approaches with extensive cross-validation studies. The results show that the Buckley-James based partial least squares, stepwise subset model selection and principal components regression have similar promising predictive power and the partial least square method has several advantages in terms of interpretability and numerical computation.

HIV Infections↗
PubMed1985

Analysis of biomedical signals by means of linear modeling.

The recording and subsequent analysis of electrical signals of physiological origin constitutes an important aspect of current biomedical research. A versatile method for the analysis of such signals is based on linear, i.e., autoregressive (moving average) modeling. These techniques are based on fitting a hypothetical model to the signal under observation. These models are capable of generating the original signal by a linear combination of past observations and past and present noise samples. High resolution spectral estimates can be obtained in this way. Also, the often small number of model coefficients offer a concise description of the signal and may be used for classification purposes. Other applications entail the detection of nonstationarities, data-compression, and signal enhancement. In this review, linear modeling methods for the analysis of electroencephalograms, electro- and phono-cardiograms, electromyograms, and gastrointestinal signals are surveyed.

Digestive System Physiological Phenomena↗
PubMed2001

Impact of pharmacokinetic-pharmacodynamic model linearization on the accuracy of population information matrix and optimal design.

Influence of experimental design on hyperparameter estimates precision when performing a population pharmacokinetic-pharmacodynamic (PK-PD) analysis has been shown by several studies and various approaches have been proposed for optimizing or evaluating such designs. Some of these methods rely on the optimization of a suitable scalar function of the population information matrix. Unfortunately for the nonlinear models encountered in pharmacokinetics or pharmacodynamics the latter is particularly difficult to evaluate. Under some assumptions and after a linearization of the PK-PD model a closed form of this matrix can be obtained which considerably simplifies its calculation but leads to an approximation. The aim of this paper is to evaluate the quality of the latter and its potential impact, when comparing or optimizing population designs and to relate it to Bates and Watts curvature measures. Two models commonly used in PK-PD were considered and nominal hyperparameter values when chosen for each one. Several population designs were studied and the associated population information matrix was computed for each using the approximate procedure and also using a reference method. Design optimizations were calculated under constraints for each model from the reference and approximate population information matrix. Nonlinearity curvatures were also computed for every model and design. The impact of model linearization when calculating the population information matrix was then examined in terms of lower bound accuracies on the hyperparameter estimates, design criterion variation, as well as D-optimal population designs, these results being related to nonlinearity curvature measures. Our results emphasize the influence of the parameter effects curvature when deriving the lower bounds of the hyperparameter estimates precision for a given design from the approximate population information matrix especially for hyperparameters quantifying the PK-PD interindividual variability. No discrepancies were detected between the population D-optimal designs obtained from the approximate and reference matrix despite some minor differences in criterion variation with respect to the design. More pronounced differences were, however, observed when comparing the amplitudes of criterion variation which can lead to errors when calculating design efficiencies. From a practical point of view, a strategy easily applicable by the pharmacokineticist for avoiding such problems in the context of population design optimization or comparison is then proposed.

Humans↗