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At least 595 records · Page 33Linked to original sources
PubMed2000

Estimating particulate matter-mortality dose-response curves and threshold levels: an analysis of daily time-series for the 20 largest US cities.

Numerous studies have shown a positive association between daily mortality and particulate air pollution, even at concentrations below regulatory limits. These findings have motivated interest in the shape of the exposure-response relation. The authors have developed flexible modeling strategies for time-series data that include spline and threshold exposure-response models; they apply these models to daily time-series data for the 20 largest US cities for 1987-1994, using the concentration of particulate matter <10 microm in aerodynamic diameter (PM10) as the exposure measure. The spline model showed a linear relation without indication of threshold for PM10 and relative risk of death for all causes and cardiorespiratory causes; by contrast, for other causes, the risk did not increase until approximately 50 microg/m3 PM10. For all-cause mortality, a linear model without threshold was preferred to the threshold model and to the spline model, using the Akaike information criterion (AIC). The findings were similar for cardiovascular and respiratory deaths combined. By contrast, for causes other than cardiovascular and respiratory, a threshold model was more competitive with a threshold value estimated at 65 microg/m3. These findings indicate that linear models without a threshold are appropriate for assessing the effect of particulate air pollution on daily mortality even at current levels.

Air Pollutants↗
PubMed1995

MicroPharm-K, a microcomputer interactive program for the analysis and simulation of pharmacokinetic processes.

PURPOSE: The microcomputer program, MicroPharm-K (MP-K) was developed for pharmacokinetic modeling, including analysis of experimental data and estimation of relevant parameters, and simulation. The intention was to provide a user-friendly, interactive, event-driven program for PC computers. METHODS: The data are ascribed to a predefined model from a library including various routes of administration, oral or intra-venous, bolus or infusion, and various compartmental interpretations, 1 to 3. Single and multiple administrations are supported. The program provides initial estimates of the parameters in most cases, and the parameters are then fitted to the model by non linear model fitting using either the Simplex, Evol, Gauss-Newton, Levenberg-Marquardt or Fletcher-Powell algorithms. The non linear model fitting is based on the maximum likelihood method, and the criterion to minimize is either the weighted least squares (Chi 2 criterion) or the extended least squares. Graphical representations of non-fitted or curve-fitted data are immediately available (including log-scale representation), as well as pharmacokinetic typical parameters such as area under the curve, clearance, volumes, time-rate constants, transfer rate constants, etc. RESULTS: Simulated and experimental data were analysed and the results were similar to those obtained by other programs. CONCLUSIONS: This non linear fitting program has been proved in our laboratory to be a very effective package for pharmacokinetic studies, including estimation and simulation. Because it is easy-to-use and runs on basic computers, the program could also be used for educational purposes.

Administration, Oral↗
PubMed2006

Maximal oxygen uptake at the same fat-free mass is greater in men than women.

Maximal oxygen uptake (VO(2max)) is commonly divided by body mass or fat-free mass (body mass minus fat mass) in order to make it size independent so that comparisons among persons of different size can be made. However, numerous studies have shown that the ratio created is not size-independent. Analysis of covariance (ANCOVA) allows a dependent variable to be compared between groups at a common value of a covariate. The purpose of this study was to compare VO(2max) at the same fat-free mass (FFM) in 230 sedentary subjects (half men) who ranged in age from 20 to 70 years. The subjects underwent maximal cardiopulmonary exercise testing on a cycle ergometer as ventilation and the expired gas fractions were being measured. Two ANCOVA models were evaluated. The dependent variable, fixed factor and covariate(s) in the linear model were VO(2max), sex and FFM, respectively. The corresponding terms in the log-linear model were ln VO(2max), sex, and ln FFM and age. Sex made a significant contribution to both models. In the linear model, the mean VO(2max) at the same FFM was 27% higher in men (2,444 versus 1,929 ml min(-1); P<0.001). In the log-linear model, the corresponding value at the same FFM and age was 32% higher in men (2,368 versus 1,794 ml min(-1); P<0.001). The goodness of fit indices of squared multiple correlation coefficient and standard error of estimate were significantly better for the log-linear model. We conclude that VO(2max) at the same FFM is considerably higher in men than in women who have a sedentary lifestyle.

Adult↗
PubMed1998

Constructive incremental learning from only local information

We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, the size and shape of the receptive field of each locally linear model, as well as the parameters of the locally linear model itself, are learned independently, that is, without the need for competition or any other kind of communication. Independent learning is accomplished by incrementally minimizing a weighted local cross-validation error. As a result, we obtain a learning system that can allocate resources as needed while dealing with the bias-variance dilemma in a principled way. The spatial localization of the linear models increases robustness toward negative interference. Our learning system can be interpreted as a nonparametric adaptive bandwidth smoother, as a mixture of experts where the experts are trained in isolation, and as a learning system that profits from combining independent expert knowledge on the same problem. This article illustrates the potential learning capabilities of purely local learning and offers an interesting and powerful approach to learning with receptive fields.

Journal Article↗
PubMed2004

The National Morbidity, Mortality, and Air Pollution Study. Part III: PM10 concentration-response curves and thresholds for the 20 largest US cities.

Numerous studies have shown a positive association between daily mortality and particulate air pollution, even at concentrations below regulatory limits. These findings have motivated interest in the shape of the concentration-response relation. We developed flexible modeling strategies for time-series data that include spline and threshold concentration-response models. We applied these models to daily time-series data for the 20 largest US cities for 1987 through 1994, using concentration of particulate matter less than 10 microm in aerodynamic diameter (PM10*) as the exposure measure. The spline model showed a linear relation without indicating a threshold for the relative risks of death for all causes (total deaths) and for cardiovascular-respiratory causes in relation to PM10 concentration. By contrast, for causes other than cardiovascular-respiratory, the relative risk did not increase until the concentration reached approximately 50 microg/m3 PM10. For total mortality, a linear model without threshold was preferred to the threshold model and to the spline model, using the value of the Akaike information criterion (AIC). The findings were similar for combined cardiovascular and respiratory deaths. These findings indicate that linear models without a threshold are appropriate for assessing the effect of particulate air pollution on daily mortality even at current ambient levels.

Air Pollution↗
PubMed2002

Experimental determination of the anisotropy function for the model 200 103Pd "light seed" and derivation of the anisotropy constant based upon the linear quadratic model.

Since the publication of the AAPM Task Group 43 report in 1995, Model 200 103Pd seed, which has been widely used in prostate seed implants and other brachytherapy procedures, has undergone some changes in its internal geometry resulting from the manufacturer's transition from lower specific activity reactor-produced 103Pd ("heavy seeds") to higher specific activity accelerator-produced radioactive material ("light seeds"). Based on previously reported theoretical calculations and measurements, the dose rate constants and the radial dose functions of the two types of seeds are nearly the same and have already been reported. In this work, the anisotropy function of the "light seed" was experimentally measured and an averaging method for the determination of the anisotropy constant from distance-dependent values of anisotropy factors is presented based upon the continuous low dose rate irradiation linear quadratic model for cell killing. The anisotropy function of Model 200 103Pd "light seeds" was measured in a Solid Water phantom using 1 X 1 x 1 mm micro LiF TLD chips at radial distances of 1, 2, 3, 4, 5, and 6 cm and at angles from 0 to 90 degrees with respect to the longitudinal axis of the seeds. At a radial distance of 1 cm, the measured anisotropy function of the 103Pd "light seed" is considerably lower than that of the 103Pd "heavy seed" reported in the TG 43 report. Our measured values at all radial distances are in excellent agreement with the results of a Monte Carlo simulation reported by Weaver, except for points along and near the seed longitudinal axis. The anisotropy constant of the 103Pd "light seed" was calculated using the linear quadratic biological model for cell killing in 30 clinical implants. For the model 200 "light seed," it has a value of 0.865. However, our biological model calculations lead us to conclude that if the anisotropy factors of an interstitial brachytherapy seed vary significantly over radial distances anisotropy constant should not be used as an approximation for anisotropy characteristics of a brachytherapy seed.

Anisotropy↗
PubMed2001

Trabecular bone's mechanical properties are affected by its non-uniform mineral distribution.

The bone remodeling process takes place at the surface of trabeculae and results in a non-uniform mineral distribution. This will affect the mechanical properties of cancellous bone, because the properties of bone tissue depend on its mineral content. We investigated how large this effect is by simulating several non-uniform mineral distributions in 3D finite element models of human trabecular bone and calculating the apparent stiffness of these models. In the 'linear model' we assumed a linear relation between mineral content and Young's modulus of the tissue. In the 'exponential model' we included an empirical exponential relation in the model. When the linear model was used the mineral distribution slightly changed the apparent stiffness, the difference varied between an 8% decrease and a 4% increase compared to the uniform model with the same BMD. The exponential model resulted in up to 20% increased apparent stiffness in the main load-bearing direction. A thin less mineralized surface layer (28 microm) and highly mineralized interstitial bone (mimicking mineralization resulting from anti-resorptive treatment) resulted in the highest stiffness. This could explain large reductions in fracture risk resulting from small increases in BMD. The non-uniform mineral distribution could also explain why bone tissue stiffness determined using nano-indentation is usually higher than finite element (FE)-determined stiffness. We conclude that the non-uniform mineral distribution in trabeculae does affect the mechanical properties of cancellous bone and that the tissue stiffness determined using FE-modeling could be improved by including detailed information about mineral distribution in trabeculae in the models.

Adult↗
PubMed1996

Linear multivariate models for physiological signal analysis: applications.

Some applications of linear multivariate modelling methods in the analysis of physiological signals are presented. These applications illustrate the methods in the analysis of cardiovascular dynamics, which has been one of the main application fields of the multivariate modelling during the last ten years. It is demonstrated that physiologically meaningful information about the causal interactions in the cardiovascular system can be drawn from the routinely available clinical signals. Both static and dynamic conditions are considered.

Humans↗
PubMed2001

Methodological approaches to conducting pooled cross-sectional time series analysis: the example of the association between all-cause mortality and per capita alcohol consumption for men in 15 European states.

AIM: To compare different statistical models in order to estimate the association of alcohol consumption and total mortality when time series data stem from different regions. DATA AND METHODS: Data on per capita consumption in 15 European countries were combined with standardized mortality rates covering different periods between 1950 and 1995. An indicator of region-specific drinking patterns was measured without reference to a concrete time point, thus generating a hierarchical data structure. Two groups of models were compared: pooled cross-sectional time series models with different error structures and hierarchical linear models (random coefficient models). RESULTS: If historical time is not controlled for in cross-sectional models, this might result in estimating a negative association between alcohol consumption and total mortality. Hierarchical linear models or cross-sectional models controlling for historical time, however, resulted in the expected positive association. Only hierarchical linear models were able to adequately estimate the moderating effect of drinking patterns on the association between alcohol consumption and total mortality. CONCLUSION: For pooled cross-sectional time series data, control for the potential impact of historical time is of utmost importance. Hierarchical linear models constitute a superior alternative to analyze such complex data sets, especially as time-independent characteristics of regions can be implemented in the model.

Alcohol Drinking↗
PubMedDate not supplied

A non-linear mathematical model for computerized analysis of mood curves: construction of the model and its application to the mood curves of depressive and schizophrenic inpatients.

A non-linear mathematical model for the computerized description of mood curves is presented. This model reaches a high goodness of fit to the real data and seems superior to a linear model recently proposed. Using this model in a computer program for describing the mood data of a large sample of psychiatric inpatients, significant and clinically meaningful group differences between the mood curves of schizophrenic, endogenous depressive, and neurotic depressive inpatients could be demonstrated. The application of this methodology might be helpful among others in the field of evaluative research.

Antidepressive Agents↗
PubMed2005

Structural mean models for compliance analysis in randomized clinical trials and the impact of errors on measures of exposure.

Partial compliance with assigned treatment regimes is common in drug trials and calls for a causal analysis of the effect of treatment actually received. As such observed exposure is no longer randomized, selection bias must be carefully accounted for. The framework of potential outcomes allows this by defining a subject-specific treatment-free reference outcome, which may be latent and is modelled in relation to the observed (treated) data. Causal parameters enter these structural models explicitly. In this paper we review recent progress in randomization-based inference for structural mean modelling, from the additive linear model to the structural generalized linear models. An arsenal of tools currently available for standard association regression has steadily been developed in the structural setting, providing many parallel features to help randomization-based inference. We argue that measurement error on exposure is an important practical complication that has, however, not yet been addressed. We show how standard additive linear structural mean models are robust against unbiased measurement error and how efficient, asymptotically unbiased inference can be drawn when the degree of measurement error bias is known. The impact of measurement error is illustrated in a blood pressure example and finite sample properties are verified by simulation. We end with a plea for more and careful use of this methodology and point to directions for further development.

Guideline Adherence↗
PubMed2001

The use of the linear quadratic model in radiotherapy: a review.

To be able to predict the impact of any radiotherapy treatment the physics of radiation interactions and the expected biological effect for any radiotherapy treatment situation (dose, fractionation, modality) must be both understood and modelled. This review considers the current use and accuracy of the linear quadratic model which can be used to consider the variation in tissue response with fraction size. Cell kill following radiation damage results from damage to the DNA which can take a variety of forms. In many cases the linear quadratic model is used to estimate the relative impact for different situations especially clinical studies relating to fraction size. This is mainly undertaken using parameters derived from the linear quadratic model such as biological effective dose and standard effective dose. The model has also been adapted to consider the effect of overall treatment time, repair during treatment (as occurs for brachytherapy treatments) and other situations. There are some concerns over its use, mainly in the small dose ranges (both total low doses and low doses per fraction) where studies have shown its inaccuracy. In other situations however it does appear to provide a reasonable estimate of relative clinical effect. As with all models, however results should never be considered out of clinical context.

Biophysical Phenomena↗
PubMed2003

Design and quality control issues related to dietary assessment, randomized clinical trials and meta-analysis of field-based studies in developing countries.

The essential design and quality control aspects of dietary assessment, randomized controlled trials (RCT) in developing countries and their subsequent meta-analysis are reviewed. Dietary assessment protocols consist of three stages: measurement of food intakes using a method appropriate for the study objectives, calculation of nutrient intakes and finally an evaluation of their nutrient adequacy. The latter may involve: 1) assessment of dietary diversity (average number of different foods consumed per day) and selected indices of dietary quality such as intakes of animal source foods (g/d); 2) percentage of energy from protein, fat and animal protein; 3) selected nutrient densities; and 4) dietary phytate:zinc molar ratio, as well as the prevalence of inadequate nutrient intakes calculated using a probability approach. To establish causality between the nutrient inadequacies identified and adverse health outcomes, RCT must be undertaken. A prerequisite of RCT is double-blind randomization, a procedure designed to eliminate biases arising from baseline confounding variables, unintended interventions and ascertainment bias. Results from existing RCT can be summarized via meta-analysis to gain a better understanding of the population relationship. Meta-analysis is a statistical technique involving linear models or generalized linear models, which can be performed after locating the individual studies, and selecting and abstracting all the necessary data.

Developing Countries↗
PubMed2005

Individual QT-R-R relationship: average stability over time does not rule out an individual residual variability: implication for the assessment of drug effect on the QT interval.

BACKGROUND: Universal QT correction formulae have been shown to under or overcorrect the QT interval duration. Individual QT-R-R modeling has been proposed as a preferable solution for heart rate correction of QT intervals. However, the QT-R-R relationship stability over time needs to be evaluated. METHODS: The present report is part of randomized, double-dummy, and placebo-controlled 4-way crossover phase 1 study (48 healthy volunteers). Each randomized period included a run-in placebo day followed the day after by drug administration, with moxifloxacin as a positive control for QT interval measurement. Digital Holter ECG data were analyzed using the "bin" approach. For each period, individual QT-R-R relationship were calculated using two different models (linear and parabolic log-log models). RESULTS: The mean intrasubject variability for the alpha coefficient of the linear modeling (SDintra = 0.011 +/- 0.005) reached 28.6 +/- 10.2%. When the parabolic model was considered, the SDintra was 0.026 +/- 0.009 for the alpha coefficient. The QT-R-R relationship variability was in part related to long-term RR changes (R2 = 30%, P < 0.05). However, no significant time effect (ANOVA) was evidenced for QT-R-R coefficients. Moxifloxacin significantly increased the alpha coefficient of the QT-R-R relationship from 0.07 +/- 0.018 to 0.085 +/- 0.019, P < 0.05 (linear model). CONCLUSIONS: The individual QT-R-R relationship shows a residual variability in part related to long-term autonomic changes. In addition, the QT-R-R relationship might be modulated by the drug tested. As a consequence, pretherapy QT-R-R relationship obtained in a given patient cannot be used as a fingerprint throughout a drug trial.

Aza Compounds↗
PubMed1982

Evidence for turnover of DNA in the nucleus and in the cytoplasm of adult post-mitotic neurons in vivo.

Experiments were designed to determine if turnover of DNA occurs in post-mitotic neurons of adult mammals. Three-month-old laboratory mice, Mus musculus, and white footed mice, Peromyscus leucopus, were given three injections of 3H-thymidine (3H-TdR, 16 muCi/g body weight) at noon, 4 pm and 8 pm, and were killed serially beginning 1 h after the last injection. The brain stem was removed from the animals and autoradiographed. Some of the sections were treated with DNase prior to autoradiography. A sensitive and accurate method of autoradiographic grain count analysis was used to measure the grain counts over areas of neuron nuclei, neuron cytoplasm and background areas of the slide (Cameron, Pool and Hoage, 1979). There was a significant elevation of grains per unit area above background over both the nucleus and the cytoplasm in animals killed 24 h after the last 3H-TdR injection. Counts were reduced to background level by prior DNase treatment. This shows that the grains over the nucleus and the cytoplasm were due to label in the DNA. Changes in the grain counts with time were subjected to least squares regression analysis. In the mouse both the nucleus and the cytoplasmic grain counts showed a decreasing slope which was significantly different from a slope of zero when the data were fitted to either a linear model or a log-linear model. The data showed a somewhat better fit to the log-linear model. The labelled nuclear DNA in the mouse had a calculated half-life of about 502 h while the labelled cytoplasmic DNA had a calculated half-life of about 97 h. Our ability to measure and to characterize DNA turnover in specific in vivo cell types allows us to test theories about its functional role.

Animals↗
PubMed2005

Modelling the natural history of geographic atrophy in patients with age-related macular degeneration.

PURPOSE: To model the natural course of geographic atrophy (GA) in patients with age-related macular degeneration (AMD). METHODS: Data on the natural course of GA were collected in the multi-center, longitudinal, prospective observational FAM study. The size of GA was measured by autofluorescence scanning laser ophthalmoscopy. The natural course of GA is modelled by two different mixed effect models (MEM). Both models are compared with respect to the correctness of the model assumptions, goodness of fit, and predictive behavior. RESULTS: The linear model results in better prediction, the non-linear model is more in agreement with the model assumptions. The non-linear model fits the data for small and large areas of GA better, while the linear model seems to be more adequate for the medial areas. More data will be needed to study the interplay of both models in more detail. CONCLUSIONS: The natural course of GA varies extremely between individuals. However, reliable factors for the explanation of this variability have so far not been established. MEM are useful for describing "inter-individual" as well as "intra-individual" influences without the need for precise knowledge of the influencing factors. Using MEM to evaluate data on the natural history of GA allows one to derive parameter estimates, which could be used to design interventional trials for modes of therapy with a potential to reduce or stop the progression of GA in patients with AMD.

Aged↗