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Results for “Models, Statistical”

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At least 307 records · Page 17Linked to original sources

PubMed2005

Statistical modelling of fatigue-related electromyographic median frequency characteristics of back and hip muscles during a standardized isometric back extension test.

The purpose of the present study was to evaluate which statistical model - linear, logarithmic, quadratic or exponential - best described the fatigue-related electromyographic (EMG) changes of back and hip muscles. Twenty healthy volunteers performed a modified Biering-Sorensen test. The EMG activity of the latissimus dorsi (LD), longissimus thoracis pars thoracis (LTT) and lumborum (LTL), iliocostalis lumborum pars thoracis (ILT) and lumborum (ILL), multifidus (MF), gluteus maximus (GM) and biceps femoris (BF) was measured bilaterally using surface electrodes. Higher R(2) values were found for the quadratic models (p<0.05 for all muscles), and lower R(2) values for the logarithmic models (p<0.05 for LTT, LTL, ILL, MF and GM). The exponential models generated higher R(2) values compared to the linear ones for the LTT, LTL and MF (all p<0.05). Further analyses revealed, however, that these models did not add useful additional information, and therefore would only increase the complexity. The findings of the current study validate the use of simple linear regression techniques when studying fatigue-related EMG median frequency characteristics of back and hip muscles during isometric contractions.

Adult↗

PubMed1995

Identifying chromosomal fragile sites from individuals: a multinomial statistical model.

The inability to identify fragile sites from data for single individuals remains the major obstacle to determining whether these chromosomal loci are predisposed to cancer-causing and evolutionary rearrangements. We describe a novel statistical model that is amenable to data from single individuals and that establishes site-specific chromosomal breakage as nonrandom with respect to the distribution of total breakage. Our method tests incrementally smaller subsets of the data for homogeneity under a multinomial model that assigns equal probabilities to a maximal set of nonfragile sites and unrestricted probabilities to the remaining fragile sites with significantly higher numbers of breaks. We show how standardized Pearson's chi-square (X2) and likelihood-ratio (G2) statistics can be appropriately used to measure goodness-of-fit for sparse contingency (individual-based) data in this model. A sample application of this approach indicates extensive variation in fragile sites among individuals and marked differences in fragile-site inferences from pooled as opposed to per-individual data.

Animals↗

PubMed1981

A statistical model and analysis for genetic and environmental effects in responses from twin-family studies.

A statistical model and analysis for genetic and environmental effects in twin-family data are presented. The model is used to derive expressions for phenotypic correlations of 22 essential pair relationships in twin-family units. The analysis proceeds in two steps. First, differential effects of sex, generation, and sex-zygosity of twin-family units and correlations due to cluster sampling are eliminated from correlation data. Then, estimates and tests of model parameters are calculated from the adjusted data. The theory and methods were developed for a Swedish twin-family study of many behaviors possibly related to the smoking habit. There, it is important to screen for behaviors that clearly are under genetic control and to assess relative influences of various biological and social environments on the development of all behaviors. Height data from the Swedish study are used to illustrate concepts and methods presented in this paper.

Body Height↗

PubMedDate not supplied

The construction and assessment of a statistical model for the prediction of protein assay data.

The focus of this work is the development of a statistical model for a bioinformatics database whose distinctive structure makes model assessment an interesting and challenging problem. The key components of the statistical methodology, including a fast approximation to the singular value decomposition and the use of adaptive spline modeling and tree-based methods, are described, and preliminary results are presented. These results are shown to compare favorably to selected results achieved using comparitive methods. An attempt to determine the predictive ability of the model through the use of cross-validation experiments is discussed. In conclusion a synopsis of the results of these experiments and their implications for the analysis of bioinformatic databases in general is presented.

Computational Biology↗

PubMed1990

Statistical models for data from periodontal research.

Many factors have been hypothesised either to characterise groups and individuals at risk for periodontal disease or to be markers of periodontal breakdown. In order to identify these as associated either with disease status or progression, a statistical association between the factor and a measure of disease will have to be demonstrated. The statistical modelling of data arising from periodontal research presents special problems. These include the large number of measurements made in each subject, the large magnitude of measurement error compared to the changes in attachment level, the analysis of longitudinal studies, the lack of a measure of instantaneous rate of attachment loss and controversies over the nature of the progression of the disease. We consider statistical methods currently available in the light of these difficulties and identify areas in which further research is necessary.

Analysis of Variance↗

PubMed2004

A statistical model for functional mapping of quantitative trait loci regulating drug response.

Differential drug response, that is, pharmacodynamics, is most often likely to be a complex trait, controlled by the combined influences of multiple genes and environmental influences. Genetic mapping has proven to be a powerful tool for detecting and identifying specific genes affecting complex traits, that is, quantitative trait loci (QTL), based on polymorphic markers. In this article, we present a novel statistical model for genetic mapping of QTL governing pharmacodynamic processes. In principle, this model is a combination of functional mapping proposed to map function-valued traits and linkage disequilibrium mapping designed to provide high-resolution mapping of QTL by making use of recombination events created at a historic time. We implement a closed-form solution for the Expectation-Maximization algorithm to estimate the population genetic parameters of QTL and the simplex algorithm to estimate the curve parameters describing the pharmacodynamic changes of different QTL genotypes in response to drug dose or concentrations. Extensive simulations are performed to investigate the statistical properties of our model. The implications of our model in pharmacogenetic and pharmacogenomic research are discussed.

Algorithms↗

PubMed1994

Dose-response assessment for developmental toxicity. III. Statistical models.

Although quantitative modeling has been central to cancer risk assessment for years, the concept of dose-response modeling for developmental effects is relatively new. The benchmark dose (BMD) approach has been proposed for use with developmental (as well as other noncancer) endpoints for determining reference doses and reference concentrations. Statistical models appropriate for representing the unique features of developmental toxicity testing have been developed and applied (K. Rai and J. Van Ryzin, 1985, Biometrics 41, 1-9; L. Kupper, C. Portier, M. Hogan, and E. Yamamoto, 1986, Biometrics 42, 85-98; R. Kodell, R. Howe, J. Chen, and D. Gaylor, 1991, Risk Anal. 11, 583-590). Generalizations of those models (designated the RVR, LOG, and NCTR models, respectively) account for the correlations among observations in individual fetuses or implant within litters; the potential for variables other than dose, such as litter size, to affect the probability of adverse outcome; and the possibility of a threshold dose below which background response rates are unaltered. The generalized models were applied to a database of 607 endpoints with significant dose-related increases in response rate. It was determined that the models were generally capable of fitting the observed dose-response patterns, with the LOG model appearing to be superior with respect to fit. A significant contributor to the ability of the LOG model to fit the data was its flexibility with respect to the representation of the dependence of response probability on litter size, a trait not shared by the other two models. Litter size appeared to be a significant covariable for predicting response rates, even when intralitter correlation was accounted for by assuming a beta-binomial distribution for the observations among individual fetuses. In contrast, a threshold dose parameter did not appear to be necessary to adequately describe the observed dose-response patterns. BMD estimates (corresponding to 5% additional risk) from all three models were similar to one another and to BMDs estimated from other, generic dose-response models (not specifically designed for developmental toxicity testing) that modeled average proportion of fetuses affected. The BMDs at the 5% level of risk were similar to no observed adverse effect levels determined by statistical tests of trend. Greater emphasis on and further examination of dose-response modeling for developmental toxicity testing are needed; biologically based approaches that consider the continuum of developmental effects induced in such tests should be encouraged.

Animals↗

PubMed1988

A statistical model for pain in patient-controlled analgesia and conventional intramuscular opioid regimens.

A statistical model was developed: 1) to compare the efficacy of patient-controlled analgesia (PCA) and traditional intramuscular (IM) opioids for pain relief in 40 patients after total knee replacement and, 2) to evaluate pain cycles associated with each technique. Hourly visual analog pain scores were subjected to two-way analysis of variance (ANOVA) and time-series analysis. Hourly verbal analog pain scores were used to determine predominant pain levels. According to ANOVA, PCA was no more effective than were IM opioids. Time-series analyses documented a complete cycle of pain every 5.3 hours in patients receiving IM opioids but no pain cycle with use of PCA. Analysis of PCA verbal analog scores demonstrated self-administration of opioids to "moderate" levels of pain relief with use of PCA, not to complete analgesia. These results suggest that certain patients may not envision complete postoperative analgesia as being possible. Hence, they self-administer opioids for pain relief with PCA according to their expectations. Population characteristics may modify PCA efficacy. These characteristics should be delineated and the use of PCA targeted to appropriate patients.

Aged↗

PubMed1998

The design and analysis of longitudinal studies of development and psychopathology in context: statistical models and methodological recommendations.

The utility and flexibility of recent advances in statistical methods for the quantitative analysis of developmental data--in particular, the methods of individual growth modeling and survival analysis--are unquestioned by methodologists, but have yet to have a major impact on empirical research within the field of developmental psychopathology and elsewhere. In this paper, we show how these new methods provide developmental psychopathologists with powerful ways of answering their research questions about systematic changes over time in individual behavior and about the occurrence and timing of life events. In the first section, we present a descriptive overview of each method by illustrating the types of research questions that each method can address, introducing the statistical models, and commenting on methods of model fitting, estimation, and interpretation. In the following three sections, we offer six concrete recommendations for developmental psychopathologists hoping to use these methods. First, we recommend that when designing studies, investigators should increase the number of waves of data they collect and consider the use of accelerated longitudinal designs. Second, we recommend that when selecting measurement strategies, investigators should strive to collect equatable data prospectively on all time-varying measures and should never standardize their measures before analysis. Third, we recommend that when specifying statistical models, researchers should consider a variety of alternative specifications for the time predictor and should test for interactions among predictors, particularly interactions between substantive predictors and time. Our goal throughout is to show that these methods are essential tools for answering questions about life-span developmental processes in both normal and atypical populations and that their proper use will help developmental psychopathologists and others illuminate how important contextual variables contribute to various pathways of development.

Adolescent↗

PubMed2006

Performance of statistical models to predict mental health and substance abuse cost.

BACKGROUND: Providers use risk-adjustment systems to help manage healthcare costs. Typically, ordinary least squares (OLS) models on either untransformed or log-transformed cost are used. We examine the predictive ability of several statistical models, demonstrate how model choice depends on the goal for the predictive model, and examine whether building models on samples of the data affects model choice. METHODS: Our sample consisted of 525,620 Veterans Health Administration patients with mental health (MH) or substance abuse (SA) diagnoses who incurred costs during fiscal year 1999. We tested two models on a transformation of cost: a Log Normal model and a Square-root Normal model, and three generalized linear models on untransformed cost, defined by distributional assumption and link function: Normal with identity link (OLS); Gamma with log link; and Gamma with square-root link. Risk-adjusters included age, sex, and 12 MH/SA categories. To determine the best model among the entire dataset, predictive ability was evaluated using root mean square error (RMSE), mean absolute prediction error (MAPE), and predictive ratios of predicted to observed cost (PR) among deciles of predicted cost, by comparing point estimates and 95% bias-corrected bootstrap confidence intervals. To study the effect of analyzing a random sample of the population on model choice, we re-computed these statistics using random samples beginning with 5,000 patients and ending with the entire sample. RESULTS: The Square-root Normal model had the lowest estimates of the RMSE and MAPE, with bootstrap confidence intervals that were always lower than those for the other models. The Gamma with square-root link was best as measured by the PRs. The choice of best model could vary if smaller samples were used and the Gamma with square-root link model had convergence problems with small samples. CONCLUSION: Models with square-root transformation or link fit the data best. This function (whether used as transformation or as a link) seems to help deal with the high comorbidity of this population by introducing a form of interaction. The Gamma distribution helps with the long tail of the distribution. However, the Normal distribution is suitable if the correct transformation of the outcome is used.

Aged↗

PubMed1993

Modelling lactation using an inverse polynomial in a multilevel statistical model.

A method is outlined for modelling the relationship between breast-milk production and time from delivery, suitable for data collected longitudinally. The relationship is described using inverse polynomials. In a two-level model the coefficients are specified as random variables across subjects, allowing within- and between-subject variation to be separately estimated. In a three-level model, day-to-day variation can also be separately estimated. The model is illustrated using data from the Chiang Mai Lactation Project. It allows the use of covariates that vary over time, and provides a method of analysis that is compatible with the practical constraints on the timing of data collection that are inherent in longitudinal studies.

Age Factors↗

PubMed2007

Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor.

Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and so on. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearity; (2) multiple models with a posterior probability for modeling different operating regions; (3) local batch monitoring by the T(2)- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then, these local regions can be supervised separately, leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model.

Biodegradation, Environmental↗

PubMed1992

Multivariate statistical modeling: alternative approach to test evaluation, applied to counting reticulocytes by flow cytometry.

We present a statistical path analysis model for the evaluation of two tests in the absence of a "gold standard" method. This model is applied to the evaluation of flow-cytometric and visual reticulocyte counting by using as the comparison method a combination of three hematological measurements: hemoglobin concentration (HGB), mean cellular volume (MCV), and erythrocyte density width (EDW). We assumed that, in general, a higher reticulocyte count is associated with a lower HGB value and with greater values for MCV and EDW. Applying this assumption and the statistical model, we demonstrated that flow cytometry was superior to visual reticulocyte counting in the low-value range studied. The path analysis model is potentially applicable in other cases where two tests are to be compared, and when no gold standard is available.

Erythrocyte Count↗

PubMed2003

Statistical models for analyzing repeated quality measurements of horticultural products. Model evaluations and practical example.

In the field of postharvest quality assessment of horticultural products, research on the development of non-destructive quality sensors, replacing destructive and often time consuming sensors, has spurred in the last decennium offering the possibility of taking repeated quality measures on the same product. Repeated measures analysis is gaining importance during recent years and several software packages offer a broad class of routines. A dataset dealing with the postharvest quality evolution of different tomato cultivars serves as practical example for the comparison and discussion of four different statistical model types. Starting from an analysis at each time point and an ordinary least squares regression model as standard and widely used methods, this contribution aims at comparing these two methods to a repeated measures analysis and a longitudinal mixed model. It is shown that the flexibility of such a mixed model, both towards the repeated measures design of the experiments as towards the large product variability inherent to these horticultural products, is an important advantage over classical techniques. This research shows that different conclusions could be drawn depending on which technique is used due to the basic assumptions of each model and which are not always fulfilled. The results further demonstrate the flexibility of the mixed model concept. Using a mixed model for repeated measures, the different sources of variability, being inter-tomato variability, intra-tomato variability and measurement error were characterized being of great benefit to the researcher.

Computer Simulation↗

PubMed1998

Development and application of statistical models for CO concentration and duration events in the Milan urban area.

Indications for the intervention strategies to be included in the context of air quality remediation plans for the Milan metropolitan area are obtained by evaluating 5 years of hourly CO concentration data. The work reports on the analysis of standard exceedances and concentration-time trends and on the development of empirical statistical models for describing relationships between annual concentration averages and air quality-related parameters. The models are applied, in combination with simple roll-back equations, for the evaluation of emissions reduction required for standards attainment. Similar statistical approaches for describing the dependence between the duration of exceedance episodes over fixed concentration levels and simple time series parameters (annual mean concentrations) are also reported.

Air Pollutants↗

PubMed2005

Statistical modelling of the spatial distribution of prevalence of Calicophoron daubneyi infection in sheep from central Italy.

Statistical modelling for Disease Mapping and Ecological Analysis is of particular importance in veterinary parasitology because environmental characteristics can affect parasite distribution. However, the main difficulties relate to the concentration of animal populations within farms, which contrasts to the study of wild animal populations. In the present paper we report the results of a cross-sectional coprological survey designed to study the presence and distribution of the rumen fluke Calicophoron daubneyi--which causes paramphistomosis, a snail borne disease--in pastured sheep living in the Latina province of central Italy. We show how techniques derived from human epidemiology can be used to study the spatial distribution of parasite infection in animals. We proposed a hierarchical Bayesian model with random terms for unstructured variability (heterogeneity) to account for local farm characteristics and spatially structure terms (clustering) to cope with medium-large scale environmental characteristics.

Animal Husbandry↗

PubMed1990

Record linkage: statistical models for matching computer records.

"We wish to measure the evidence that a pair of records relates to the same, rather than different, individuals. The paper emphasizes statistical models which can be fitted to a file of record pairs known to be correctly matched, and then used to estimate likelihood ratios. A number of models are developed and applied to U.K. immigration statistics. The combination of likelihood ratios for possibly correlated record fields is discussed." A series of comments on the paper is also included, as well as a reply to those comments by the author (pp. 312-20).

Data Collection↗

PubMed2001

Mapping indoor radon-222 in Denmark: design and test of the statistical model used in the second nationwide survey.

In Denmark, a new survey of indoor radon-222 has been carried out, 1-year alpha track measurements (CR-39) have been made in 3019 single-family houses. There are from 3 to 23 house measurements in each of the 275 municipalities. Within each municipality, houses have been selected randomly. One important outcome of the survey is the prediction of the fraction of houses in each municipality with an annual average radon concentration above 200 Bq m(-3). To obtain the most accurate estimate and to assess the associated uncertainties, a statistical model has been developed. The purpose of this paper is to describe the design of this model, and to report results of model tests. The model is based on a transformation of the data to normality and on analytical (conditionally) unbiased estimators of the quantities of interest. Bayesian statistics are used to minimize the effect of small sample size. In each municipality, the correction is dependent on the fraction of area where sand and gravel is a dominating surface geology. The uncertainty analysis is done with a Monte-Carlo technique. It is demonstrated that the weighted sum of all municipality model estimates of fractions above 200 Bq m(-3) (3.9% with 95%-confidence interval = [3.4,4.5]) is consistent with the weighted sum of the observations for Denmark taken as a whole (4.6% with 95%-confidence interval = [3.8,5.6]). The total number of single-family houses within each municipality is used as weight. Model estimates are also found to be consistent with observations at the level of individual counties. These typically include a few hundred house measurements. These tests indicate that the model is well suited for its purpose.

Air Pollutants, Radioactive↗