SEARCH · PubMed Health

Results for “Models, Statistical”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19Linked to original sources

PubMed1987

Neuroleptic dose: a statistical model for analyzing historical trends.

Neuroleptic dosing practices during inpatient treatment of schizophrenia were examined for 1490 admission episodes during 1973 through to 1982 in two wards of a university hospital. Chlorpromazine-equivalent dose levels (CPZE) declined 50% between 1974 and 1980. As expected, length of treatment and choice of drug were both strongly related to CPZE. The general drop in CPZE is not explained by shorter treatment and changing choice of drug, however. Oral fluphenazine, haloperidol, and depot fluphenazine are used to higher maximum levels than chlorpromazine and other neuroleptics, when maximum dose is reached after one week or longer. The results illustrate that by seeking an appropriate statistical model, aggregate trends in dosing practices can be described while avoiding several of the shortcomings of earlier surveys of hospital practice.

Antipsychotic Agents↗

PubMed1977

Statistical models for renal micropuncture studies.

Statistical issues relating to data analysis of re-collection micropuncture experiments are presented. In the presence of significant animal-treatment interaction, namely, differential response of each animal at different levels of the treatment, the conventional paired or unpaired t testing would not be entirely appropriate. Accordingly, two analysis of variance (ANOVA) models have been derived for the appropriate paired and unpaired designs of micropuncture experiments. Interactive computer programs have been written for both these analyses, and the results are illustrated with experimental data. An example is presented in which the results are statistically significant with paired t testing and by analysis of variance for unequal number of tubules but not when the animal-treatment interaction is included in the analysis of variance model. To investigate linkage in renal transport mechanisms, we propose the use of partial correlation analysis. Experimental results from our laboratory are used to illustrate these techniques.

Analysis of Variance↗

PubMed2004

Minimax statistical models for air pollution time series. Application to ozone time series data measured in Bordeaux.

This paper deals with the application of l(infinity) (or minimax) optimization techniques to statistical modelling of high frequency air pollution data. The method was applied to ground-level ozone time-series data measured in Bordeaux over 4 years from 1998 to 2001. The aim of model building was to develop predictive models in order to provide forecasts of the maximal daily ground-level ozone concentration. Experimental results from this case study indicate that such techniques could be more appropriate than the commonly used l2 setting if only good estimation of high levels is of interest. When the free parameters are fitted by means of l(infinity) optimization techniques, the forecasting errors are more evenly distributed amongst the data points, resulting in a better estimation of high values. The paper compares the quality of forecasts produced by both a linear and a nonlinear model, using l2 and l(infinity) parameter optimization.

Air Pollutants↗

PubMed1998

Statistical models for low dose exposure.

Extrapolation of health risks from high to low doses has received a considerable amount of attention in carcinogenic risk assessment over decades. Fitting statistical dose-response models to experimental data collected at high doses and use of the fitted model for estimating effects at low doses lead to quite different risk predictions. Dissatisfaction with this procedure was formulated both by toxicologists who saw a deficit of biological knowledge in the models as well as by risk modelers who saw the need of mechanistically-based stochastic modeling. This contribution summarizes the present status of low dose modeling and the determination of the shape of dose-response curves. We will address the controversial issues of the appropriateness of threshold models, the estimation of no observed adverse effect levels (NOAEL), and their relevance for low dose modeling. We will distinguish between quantal dose-response models for tumor incidence and models of the more informative age/time dependent tumor incidence. The multistage model and the two-stage model of clonal expansion are considered as dose-response models accounting for biological mechanisms. Problems of the identifiability of mechanisms are addressed, the relation between administered dose and effective target dose is illustrated by examples, and the recently proposed Benchmark Dose concept for risk assessment is presented with its consequences for mechanistic modeling and statistical estimation.

Age Factors↗

PubMed1989

Sustained active ingredient release from drugs: statistical model for random sample assessment in vitro.

A method is presented according to which tolerances for active ingredient release from pharmaceutical dosage forms are calculated. The procedure is based on a statistical model. In accordance with this, the mean value is specified as a measure of the amount of active ingredient released, and the standard deviation as a measure of the uniformity of the active ingredient release. The determination of drug release tolerances is standardized. Based on clinically tested samples, changes arising from the manufacture and the storage are taken into account, thus establishing a manufacturing standard. Depending on the information available, a dynamic adaption of drug release tolerances (e.g., during the development phase) is recommended.

Delayed-Action Preparations↗

PubMed1993

The impact of counselling with a practical statistical model on patients' decision-making about treatment for epilepsy: findings from a pilot study.

To ascertain the impact of a computer-based predictive model on patients' decisions about continuing treatment of their epilepsy, 72 subjects were asked to complete questionnaires prior to and following counselling with the model. The subjects were attending the neurology out-patient clinic in a large UK hospital, and were identified from medical records as eligible for withdrawal of AEDs. The effect of counselling with the model was to make the majority of patients opt to continue AED therapy. Positive or negative framing of risk information appeared to influence the decisions of patients who were initially uncertain about continuing treatment, as did patients' perceptions of the clinician's views. The prognostic model appeared to allow relatively complex statistical information to be conveyed to patients in an accessible form. We suggest its use will aid clinicians in counselling patients and will help patients reach better-informed decisions about treatment.

Counseling↗

PubMed2001

Determining the density of states for classical statistical models: a random walk algorithm to produce a flat histogram.

We describe an efficient Monte Carlo algorithm using a random walk in energy space to obtain a very accurate estimate of the density of states for classical statistical models. The density of states is modified at each step when the energy level is visited to produce a flat histogram. By carefully controlling the modification factor, we allow the density of states to converge to the true value very quickly, even for large systems. From the density of states at the end of the random walk, we can estimate thermodynamic quantities such as internal energy and specific heat capacity by calculating canonical averages at any temperature. Using this method, we not only can avoid repeating simulations at multiple temperatures, but we can also estimate the free energy and entropy, quantities that are not directly accessible by conventional Monte Carlo simulations. This algorithm is especially useful for complex systems with a rough landscape since all possible energy levels are visited with the same probability. As with the multicanonical Monte Carlo technique, our method overcomes the tunneling barrier between coexisting phases at first-order phase transitions. In this paper, we apply our algorithm to both first- and second-order phase transitions to demonstrate its efficiency and accuracy. We obtained direct simulational estimates for the density of states for two-dimensional ten-state Potts models on lattices up to 200 x 200 and Ising models on lattices up to 256 x 256. Our simulational results are compared to both exact solutions and existing numerical data obtained using other methods. Applying this approach to a three-dimensional +/-J spin-glass model, we estimate the internal energy and entropy at zero temperature; and, using a two-dimensional random walk in energy and order-parameter space, we obtain the (rough) canonical distribution and energy landscape in order-parameter space. Preliminary data suggest that the glass transition temperature is about 1.2 and that better estimates can be obtained with more extensive application of the method. This simulational method is not restricted to energy space and can be used to calculate the density of states for any parameter by a random walk in the corresponding space.

Journal Article↗

PubMed2004

A unified statistical model for functional mapping of environment-dependent genetic expression and genotype x environment interactions for ontogenetic development.

The effects of quantitative trait loci (QTL) on phenotypic development may depend on the environment (QTL x environment interaction), other QTL (genetic epistasis), or both. In this article, we present a new statistical model for characterizing specific QTL that display environment-dependent genetic expressions and genotype x environment interactions for developmental trajectories. Our model was derived within the maximum-likelihood-based mixture model framework, incorporated by biologically meaningful growth equations and environment-dependent genetic effects of QTL, and implemented with the EM algorithm. With this model, we can characterize the dynamic patterns of genetic effects of QTL governing growth curves and estimate the global effect of the underlying QTL during the course of growth and development. In a real example with rice, our model has successfully detected several QTL that produce differences in their genetic expression between two contrasting environments. These detected QTL cause significant genotype x environment interactions for some fundamental aspects of growth trajectories. The model provides the basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments and genetic relationships for growth rates and the timing of life-history events for any organism.

Chromosome Mapping↗

PubMed1987

Serum bile-acids in liver cirrhosis: prognostic significance evidenced by a multivariate statistical model.

In order to evaluate the prognostic value of serum bile acids (SBA) in liver cirrhosis, we compared SBA levels with a theoretical expected survival length (ESL), computed on the ground of a previously proposed and validated multivariate statistical model. We demonstrated a strict correlation between SBA levels and ESL in subjects with liver cirrhosis by means of both standard linear correlation analysis and Logrank test. Our results account for a high predictive significance of SBA levels in liver cirrhosis, even in long term prognosis.

Adult↗

PubMed2005

Sensitivity of the estimated air pollution-respiratory admissions relationship to statistical model choice.

The objective of this study was to demonstrate the methodological shortcomings of currently available analytical methods for single-city time series data. We analyzed daily Chronic Obstructive Pulmonary Disease (COPD) and daily asthma hospital admissions in Melbourne, Australia from July 1989 to December 1992. Air pollution data comprised nitrogen dioxide, ozone and sulphur dioxide and air particles index consistent with particulates between 0.1 and 1 microm in aerodynamic diameter. Statistical analyses were performed using generalized linear models, generalized additive models, Poisson autoregressive models and transitional regression models. The estimated effect of nitrogen dioxide on COPD hospital admissions was similar across the different statistical models, RR = 1.06 (95% CI 1.01-1.11). Similarly the estimated effect of nitrogen dioxide on asthma hospital admissions was also consistent, RR = 1.05 (95% CI 1.01-1.09). However, the effects of ozone, air particles index and sulphur dioxide were highly sensitive to model specification for both COPD and asthma hospital admissions. In single-city studies of air pollution and respiratory disease, very different conclusions can be drawn from competing models. Furthermore, real time series data have greater complexity than any of the commonly-used existing models allow. Consequently, single-city studies should use several statistical models to demonstrate the stability of estimated effects.

Air Pollution↗

PubMed2003

Sensitivity analysis of common statistical models used to study the short-term effects of air pollution on health.

The relationship between photochemical air pollutants (nitrogen dioxide and ozone) and emergency room admissions for asthma in Madrid (Spain) for the period 1995-1998 was analysed using the statistical models commonly used to studying the short-term effects of air pollution on health: linear and Cochrane-Orcutt regression, standard Poisson and Poisson corrected by overdispersion, Poisson autoregressive models, and generalised additive models. Linear regression models presented residual autocorrelation, Poisson regression models also showed overdispersion, and generalised additive models did not show residual autocorrelation and overdispersion was substantially reduced. Linear models provided biased estimates because our health outcome is non-normally distributed. Estimates from Poisson regression allowing for overdispersion and autocorrelation did not differ substantially from those reported by generalised additive models, which present the best model fit in terms of the absence of autocorrelation and reduction of overdispersion.

Air Pollutants↗

PubMed2005

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation.

An approach to deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the desired deformation map are first obtained through tumor mass-effect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences, and the other involving tumor-induced deformation. For a new tumor case, a partial observation of the desired deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas to generate an image that is more similar to brain tumor image, thereby facilitating the atlas registration process. Results for a real and a simulated tumor case indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

Algorithms↗

PubMed2006

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation.

An approach to the deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the sought deformation map from the atlas to the image of a tumor patient are first obtained through tumor mass-effect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences in brain shape, and the other representing tumor-induced deformation. For a new tumor case, a partial observation of the sought deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas image in order to generate an image that is similar to tumor patient's image, thereby facilitating the atlas registration process. Results for a real tumor case and a number of simulated tumor cases indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

Algorithms↗

PubMed1982

Statistical models relating peak expiratory flow rates to age, height, and weight in men and women.

An epidemiological survey of 1239 normal subjects without respiratory symptoms was undertaken in an urban locality of New Delhi in 1974. The subjects were divided into males aged under or over 22 and females under or over 17. Various statistical models were calculated to relate peak expiratory flow rate to the age, height, and weight of the subjects in each group. The linear model was simple, convenient, and adequately explained the variation in peak expiratory flow rate.

Adolescent↗

PubMed2006

Selecting the right statistical model for analysis of insect count data by using information theoretic measures.

Researchers and regulatory agencies often make statistical inferences from insect count data using modelling approaches that assume homogeneous variance. Such models do not allow for formal appraisal of variability which in its different forms is the subject of interest in ecology. Therefore, the objectives of this paper were to (i) compare models suitable for handling variance heterogeneity and (ii) select optimal models to ensure valid statistical inferences from insect count data. The log-normal, standard Poisson, Poisson corrected for overdispersion, zero-inflated Poisson, the negative binomial distribution and zero-inflated negative binomial models were compared using six count datasets on foliage-dwelling insects and five families of soil-dwelling insects. Akaike's and Schwarz Bayesian information criteria were used for comparing the various models. Over 50% of the counts were zeros even in locally abundant species such as Ootheca bennigseni Weise, Mesoplatys ochroptera Stål and Diaecoderus spp. The Poisson model after correction for overdispersion and the standard negative binomial distribution model provided better description of the probability distribution of seven out of the 11 insects than the log-normal, standard Poisson, zero-inflated Poisson or zero-inflated negative binomial models. It is concluded that excess zeros and variance heterogeneity are common data phenomena in insect counts. If not properly modelled, these properties can invalidate the normal distribution assumptions resulting in biased estimation of ecological effects and jeopardizing the integrity of the scientific inferences. Therefore, it is recommended that statistical models appropriate for handling these data properties be selected using objective criteria to ensure efficient statistical inference.

Animals↗

PubMed1998

Statistical models for quantitative bioassay.

We discuss various statistical approaches useful in the analysis of nutritional dose-response data with a continuous response. The emphasis is on the multivariate case with several predictors. The methods which will be discussed can be classified into parametric models, including change-point models, and nonparametric models, which rely on smoothing methods such as weighted local linear fitting. The methods will be illustrated with the analysis of data generated from a folate depletion-repletion bioassay experiment conducted on rats, where the measured growth rate of the rate is the response variable. We also discuss the biological conclusions that can be drawn from applying various statistical methods to this data set.

Animal Nutritional Physiological Phenomena↗

PubMed1997

Statistical modeling of methane production from landfill samples.

Multiple-regression analysis was conducted to evaluate the simultaneous effects of 10 environmental factors on the rate of methane production (MR) from 38 municipal solid-waste (MSW) samples collected from the Fresh Kills landfill, which is the world's largest landfill. The analyses showed that volatile solids (VS), moisture content (MO), sulfate (SO(inf4)(sup2-)), and the cellulose-to-lignin ratio (CLR) were significantly associated with MR from refuse. The remaining six factors did not show any significant effect on MR in the presence of the four significant factors. With the consideration of all possible linear, square, and cross-product terms of the four significant variables, a second-order statistical model was developed. This model incorporated linear terms of MO, VS, SO(inf4)(sup2-), and CLR, a square term of VS (VS(sup2)), and two cross-product terms, MO x CLR and VS x CLR. This model explained 95.85% of the total variability in MR as indicated by the coefficient of determination (R(sup2) value) and predicted 87% of the observed MR. Furthermore, the t statistics and their P values of least-squares parameter estimates and the coefficients of partial determination (R values) indicated that MO contributed the most (R = 0.7832, t = 7.60, and P = 0.0001), followed by VS, SO(inf4)(sup2-), VS(sup2), MO x CLR, and VS x CLR in that order, and that CLR contributed the least (R = 0.4050, t = -3.30, and P = 0.0045) to MR. The SO(inf4)(sup2-), VS(sup2), MO x CLR, and CLR showed an inhibitory effect on MR. The final fitted model captured the trends in the data by explaining vast majority of variation in MR and successfully predicted most of the observed MR. However, more analyses with data from other landfills around the world are needed to develop a generalized model to accurately predict MSW methanogenesis.

Journal Article↗

PubMed2005

A statistical model for HIV-1 sequence classification using the subtype analyser (STAR).

MOTIVATION: HIV-1 antiretroviral drug resistance testing produces large amounts of HIV-1 protease and reverse transcriptase sequences. These provide an excellent resource to study the incidence, spread and clinical significance of HIV-1 subtypes. We have produced a program, Subtype Analyser (STAR) that rapidly and accurately subtypes HIV-1. Here we have determined a robust and statistically validated model for subtype assignment. RESULTS: We have significantly extended our HIV-1 subtyping tool (STAR), such that each query sequence when evaluated against subtype profile alignments, returns a discriminating score based on the ratio of subtype positive to negative amino acid positions. These scores were transformed into a Z-score distribution and evaluated. Of the 141 sequences used to define the subtype alignments, 98% were correctly reclassified. Inclusion of additional recombination detection within STAR increased the detection of known recombinant sequences to 95%. AVAILABILITY: STAR is available as compiled (Linux Fedora 3) or source code from http://pgv19.virol.ucl.ac.uk/download/star_linux.tar CONTACT: p.kellam@ucl.ac.uk SUPPLEMENTARY INFORMATION: http://pgv19.virol.ucl.ac.uk/download/star_supplement

Algorithms↗