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PubMed1994

Prediction of occult neck disease in laryngeal cancer by means of a logistic regression statistical model.

The ability to accurately predict the presence of subclinical metastatic neck disease in clinically N0 patients with primary epidermoid cancer of the larynx would be of great value in determining whether to perform an elective neck dissection. We describe a statistical approach to estimating the probability of occult neck disease given pretreatment clinical parameters. A retrospective study was performed involving 736 clinically N0 patients with primary laryngeal cancer who were treated surgically with primary resection and ipsilateral neck dissection. Nodal involvement was determined histologically after surgical lymphadenectomy. A logistic regression model was used to derive an equation that calculated the probability of occult neck metastasis based on pretreatment T stage, tumor location, and histologic grade. The model has a sensitivity of 74%, a specificity of 87%, and can be entered into a programmable calculator.

Adult↗
PubMed2006

On the nature of over-dispersion in motor vehicle crash prediction models.

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts--variation over and above that accounted for by the Poisson density. The extra--variation--or dispersion--is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models--tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31-40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites.

Accidents, Traffic↗
PubMed2004

Estimation of critical behavior from the density of states in classical statistical models.

We present a simple and efficient approximation scheme which greatly facilitates the extension of Wang-Landau sampling (or similar techniques) in large systems for the estimation of critical behavior. The method, presented in an algorithmic approach, is based on a very simple idea, familiar in statistical mechanics from the notion of thermodynamic equivalence of ensembles and the central limit theorem. It is illustrated that we can predict with high accuracy the critical part of the energy space and by using this restricted part we can extend our simulations to larger systems and improve the accuracy of critical parameters. It is proposed that the extensions of the finite-size critical part of the energy space, determining the specific heat, satisfy a scaling law involving the thermal critical exponent. The method is applied successfully for the estimation of the scaling behavior of specific heat of both square and simple cubic Ising lattices. The proposed scaling law is verified by estimating the thermal critical exponent from the finite-size behavior of the critical part of the energy space. The density of states of the zero-field Ising model on these lattices is obtained via a multirange Wang-Landau sampling.

Journal Article↗
PubMed1998

Statistical models for predicting a beneficial response to interferon-alpha in patients with chronic hepatitis B.

Therapy with interferon-alpha has been reported to induce remissions in 35% of patients with chronic hepatitis B. The ability to identify patients likely to respond would be helpful in making recommendations for treatment. In this statistical analysis we included 82 patients with chronic hepatitis B who received interferon-alpha in clinical trials at the National Institutes of Health between 1984 and 1991. A response was defined as the loss of hepatitis B virus (HBV) DNA and hepatitis B e antigen (HBeAg) within 1 year of therapy. Multiple clinical parameters measured at pretreatment (month 0) and after the first month (month 1) of therapy were selected by stepwise regression to support the development of the prognostic models: the two-stage logistic regression model and a neural network that utilized higher-order non-linear interactions between variables. Among the 82 patients, 24 (29%) were responders. The two-stage logistic model using pretreatment variables: sex, hepatic fibrosis and alanine aminotransferase (ALT) levels correctly identified 61% of responders and 76% of non-responders. When HBV DNA at month 1 along with sex, initial ALT and fibrosis was included, the resultant model correctly identified 69% of responders and 77% of non-responders. The neural network, by incorporating interactions between variables, correctly identified 77% and 86% of responders, and 87% and 92% of non-responders, using pretreatment factors alone and the combination of pretreatment and month 1 factors respectively. Hence, the neural network was more accurate than the simple logistic regression model in predicting a response to interferon-alpha in chronic hepatitis B. The universality of these models needs to be further verified.

Antiviral Agents↗
PubMed2001

A statistical model for length of psychiatric in-patient treatment and an analysis of contributing factors.

OBJECTIVE: Direct illness costs in psychiatry are strongly related to the length of in-patient stay (LOS). Prior studies have shown that LOS depends upon many factors; however, there is no systematic work on their interrelation and relative contribution. METHOD: A detailed statistical analysis of the factors explaining LOS for n = 4,706 consecutive admissions (1994-97) to the psychiatric hospital of the University of Tübingen is presented. RESULTS: The distribution of LOS follows an exponential decay function, suggesting a hazard-based process. Cox regression indicates that the incidence of discharge and hence LOS is modulated by a number of illness-related and other factors, and their relationship is explored. CONCLUSION: While a linear model is commonly assumed, LOS in psychiatry is governed by a hazard-based process. As a tool in quality management, LOS data for psychiatric hospitals might be routinely analyzed and the effects of non-illness-related factors minimized.

Adolescent↗
PubMed2006

Statistical models for autocorrelated count data.

A generalized linear mixed model is an increasingly popular choice for the modelling of correlated, non-normal responses in a regression setting. A number of methods are currently available for fitting a generalized linear mixed model including Monte-Carlo Markov-Chain maximum likelihood algorithms, approximate maximum likelihood (PQL), iterative bias correction, and others. Of interest in this paper is to compare the parameter estimation of the various methods in the modelling of a count data set, the incidence of polio in the USA over the period 1970-1983, using a longlinear generalized linear mixed model with an autoregressive correlation structure. Despite the fact that all of these methods are considered valid modelling techniques, we find that parameter estimates and standard errors differ substantially between analyses, particularly in the estimation of the parameters describing the random effects distribution. A small simulation study is helpful in understanding some of these differences. The methods lead to reasonably similar predictions for future observations, with small differences observed in some monthly counts.

Algorithms↗
PubMed1994

Formulation of a statistical model of heat transfer in perfused tissue.

A new model of steady-state heat transport in perfused tissue is presented. The key elements of the model are as follows: (1) a physiologically-based algorithm for simulating the geometry of a realistic vascular tree containing all thermally significant vessels in a tissue; (2) a means of solving the conjugate heat transfer problem of convection by the blood coupled to three-dimensional conduction in the extravascular tissue, and (3) a statistical interpretation of the calculated temperature field. This formulation is radically different from the widely used Pennes and Weinbaum-Jiji bio-heat transfer equations that predict a loosely defined local average tissue temperature from a local perfusion rate and a minimal representation of the vascular geometry. Instead, a probability density function for the tissue temperature is predicted, which carries information on the most probable temperature at a point and uncertainty in that temperature due to the proximity of thermally significant blood vessels. A sample implementation illustrates the dependence of the temperature distribution on the flow rate of the blood and the vascular geometry. The results show that the Pennes formulation of the bio-heat transfer equation accurately predicts the mean tissue temperature except when the arteries and veins are in closely spaced pairs. The model is useful for fundamental studies of tissue heat transport, and should extend readily to other forms of tissue transport including oxygen, nutrient, and drug transport.

Algorithms↗
PubMed2004

[Statistical models for spatial analysis in parasitology].

The simplest way to study the spatial pattern of a disease is the geographical representation of its cases (or some indicators of them) over a map. Maps based on raw data are generally "wrong" since they do not take into consideration for sampling errors. Indeed, the observed differences between areas (or points in the map) are not directly interpretable, as they derive from the composition of true, structural differences and of the noise deriving from the sampling process. This problem is well known in human epidemiology, and several solutions have been proposed to filter the signal from the noise. These statistical methods are usually referred to as Disease Mapping. In geographical analysis a first goal is to evaluate the statistical significance of the heterogeneity between areas (or points). If the test indicates rejection of the hypothesis of homogeneity the following task is to study the spatial pattern of the disease. The spatial variability of risk is usually decomposed into two terms: a spatially structured (clustering) and a non spatially structured (heterogeneity) one. The heterogeneity term reflects spatial variability due to intrinsic characteristics of the sampling units (e.g. igienic conditions of farms), while the clustering term models the association due to proximity between sampling units, that usually depends on ecological conditions that vary over the study area and that affect in similar way breedings that are close to each other. Hierarchical bayesian models are the main tool to make inference over the clustering and heterogeneity components. The results are based on the marginal posterior distributions of the parameters of the model, that are approximated by Monte Carlo Markov Chain methods. Different models can be defined depending on the terms that are considered, namely a model with only the clustering term, a model with only the heterogeneity term and a model where both are included. Model selection criteria based on a compromise between degree of complexity and goodness of fit are then needed to discriminate among them, because each specification has a different biological meaning. Our aim is to demonstrate that these techniques can be used to study the geographical distribution of a parasite infection. Our analyses are based on data collected in 142 farms of the province of Latina. In each breeding a fixed number of sheeps has been sampled (20) and checked for the presence of C. daubneyi. We have specified a Binomial model for the proportion of infected animals in each breeding. The heterogeneity component is modelled in a standard way, while we have used different prior specifications for the clustering term to show how they affect the results. When we use the usual specification also for clustering, the two models show a completely different spatial pattern of infection, probably because the intrinsic spatial structure of the clustering term tend to bias our inferences. The selection criterion indicates in this case the heterogeneity model as the "best" one. However, if we modify the prior so that a lower degree of spatial interaction is assumed, the clustering model is less complex and its goodness of fit better and it should be preferred.

Animal Husbandry↗
PubMed2005

New York's statistical model accurately predicts mortality risk for veterans who obtain private sector CABG.

OBJECTIVE: To determine whether patients' use of the Veterans Health Administration health care system (VHA) is an independent risk factor for mortality following coronary artery bypass grafting (CABG) in the private sector in New York. DATA SOURCES: VHA administrative and New York Department of Health Cardiac Surgery Reporting System (CSRS) databases for surgeries performed in 1999 and 2000. STUDY DESIGN: Prospective cohort study comparing observed, expected, and risk-adjusted mortality rates following private sector CABG for 2,326 male New York State residents aged 45 years and older who used the VHA (VHA users) and 21,607 who did not (non-VHA users). DATA COLLECTION METHODS: We linked VHA administrative databases to New York's CSRS to identify VHA users who obtained CABG in the private sector in New York in 1999 and 2000. Using CSRS risk factors and previously validated risk-adjustment model, we compared patient characteristics and expected and risk-adjusted mortality rates of VHA users to non-VHA users. PRINCIPAL FINDINGS: Compared with non-VHA users, patients undergoing private sector CABG who had used the VHA were older, had more severe cardiac disease, and were more likely to have the following comorbidities associated with increased risk of mortality: diabetes, chronic obstructive pulmonary disease, cerebrovascular disease, peripheral vascular disease, and history of stroke (p<.001 for all); a calcified aorta (p=.009); and a high creatinine level (p=.003). Observed (2.28 versus 1.80 percent) and expected (2.48 versus 1.78 percent) mortality rates were higher for VHA users than for non-VHA users. The risk-adjusted mortality rate for VHA users (1.70 percent; 95 percent confidence interval [CI]: 1.27-2.22) was not statistically different than that for the non-VHA users (1.87 percent; 95 percent CI: 1.69-2.06). Use of the VHA was not an independent risk factor for mortality in the risk-adjustment model. CONCLUSIONS: Although VHA users had a greater illness burden, use of the VHA was not found to be an independent risk factor for mortality following private sector CABG in New York. The New York Department of Health risk adjustment model adequately applies to veterans who obtain CABG in the private sector in New York.

Aged↗
PubMed1989

A statistical model to estimate variance in long term-low dose mutation assays: testing of the model in a human lymphoblastoid mutation assay.

Long term-low dose mutation assays offer a means to study the genetic effects of environmental mutagens at concentrations relevant to human exposure. These assays involve continuous induction of mutants, serial dilution of cultures and sampling to determine the mutant fraction as a function of time and mutagen concentration. An arithmetic model for the expected variance among identically treated cultures is presented. This model provides means to calculate a predicted variance of the mutant fractions and mutation rates in typical long term-low dose experiments. We have calculated the expected variances of the mutant fraction with this model and compared them to the observed variances among 4 independent experiments in which human lymphoblastoid cells were treated for 5, 10, 15 and 20 days with a non-toxic concentration of the mutagen 4-aminobiphenyl. Mutations at the HPRT locus were measured by determining the 6-thioguanine-resistant mutant fraction. The expected and observed variances of the mutant fractions are in close agreement. This model is adequate to predict the variance of the mutant fraction and should be useful in experimental design and objective evaluation of long term-low dose mutation assays.

Cells, Cultured↗
PubMedDate not supplied

Intelligent dialogue based on statistical models of clinical decision-making.

The independence Bayesian model has been used widely in computer programs designed to support clinical decision-making. A reasoning strategy has been developed to enable these programs to conduct clinically pertinent dialogue and explain their reasoning. It has been implemented in a program for the diagnosis of acute abdominal pain based on the Bayesian model of de Dombal et al. Several features of the dialogue design have been adopted from artificial intelligence research, including shared initiative and critiquing. The program adopts a flexible goal-driven strategy, attempting to confirm the clinician's diagnosis or rule out the likeliest alternative. Symptoms and signs are selected in order of their expected weights of evidence in favour of the hypothesized disease.

Abdomen↗
PubMed1998

Statistical models of transitive and intransitive dominance structures.

We present an approach based on generalized linear models for analysing dominance data. First, dominance is defined as a parameter characterizing the relationship between two individuals, determining the expected number of successes of the first individual in disputes with the other. Second, models known from the literature of two different forms of transitivity are defined in terms of these parameters, and examples of different tests of these models are given. Third, a new model is developed where the traits of individuals involved in the dominance interactions are included as covariates. Finally, we show how two forms of intransitive models of dominance structures can be constructed by including a certain interaction term between the trait variables, and terms taking into account the effect of relatedness between the individuals in the group. We reanalyse several data sets in the literature, and also discuss Appleby's method (1983, Anim. Behav., 31, 600-608), which is frequently used in analysis of dominance data. Copyright 1998 The Association for the Study of Animal Behaviour. Copyright 1998 The Association for the Study of Animal Behaviour.

Journal Article↗