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At least 145 records · Page 8Linked to original sources
PubMed1994

Statistical models for genetic susceptibility in toxicological and epidemiological investigations.

Models are presented for use in assessing genetic susceptibility to cancer (or other diseases) with animal or human data. Observations are assumed to be in the form of proportions, hence a binomial sampling distribution is considered. Generalized linear models are employed to model the response as a function of the genetic component; these include logistic and complementary log forms. Susceptibility is measured via odds ratios of response, relative to a background genetic group. Significance tests and confidence intervals for these odds ratios are based on maximum likelihood estimates of the regression parameters. Additional consideration is given to the problem of gene-environment interactions and to testing whether certain genetic identifiers/categories may be collapsed into a smaller set of categories. The collapsibility hypothesis provides an example of a mechanistic context wherein nonhierarchical models for the linear predictor can sometimes make sense.

Animals↗
PubMed2005

A statistical model for binocular rivalry.

A probabilistic model is presented for the phase durations in binocular rivalry experiments. The hypothetical construct of inhibition or reaction inhibition is used to account for the length of the successive phases of left-eye dominance and right-eye dominance. In accordance with Hull's Postulate X.B. it is assumed that the inhibition increases linearly at rate a1 during periods of left-eye dominance and decreases linearly at rate a0 during periods of right-eye dominance. Two different versions of the proposed model are presented: the beta and the Bessel inhibition models. Inhibition fluctuates between the boundaries 0 and 1 in the beta inhibition model and between -infinity and +infinity in the Bessel inhibition model. The transition rates lambda1(t) for switches from a state of left-eye dominance to a state of right-eye dominance, and lambda0(t) for switches from a state of right-eye dominance to a state of left-eye dominance depend on inhibition: lambda1(t)=l1 (Y(t)), lambda0(t)=l0(Y(t)), where l1 is a non-decreasing function and l0 is a non-increasing function. In the beta inhibition model l1(y)=c1/(1 - y) and l0(y)=c0/y. In the Bessel inhibition model l1(y)=u1e(y) and l0(y)=u0/e(y). Special attention is given to the derivation of the expectation of the stationary phase durations.

Functional Laterality↗
PubMedDate not supplied

[AIDS epidemic dynamics in the municipality of Rio de Janeiro, Brazil, 1988-1996: Spatial-temporal statistic modeling].

This study uses a spatial-temporal model to analyze the spatial spread of the AIDS epidemic (adult cases) in the municipality of Rio de Janeiro, Brazil, during three periods: 1988-1990, 1991-1993, and 1994-1996. City districts were used as the geographic units of analysis. A spatial analysis was also performed for pediatric AIDS cases due to vertical HIV transmission, according to period of birth, 1985-90 and 1991-96. For total adult AIDS cases, the initial period was characterized by a polygonal cluster located around the harbor area, which expanded from west to east. Among homosexual cases, in situ growth predominated, and a decrease in the intensity of the diffusion process was observed from the second to the final period. Among heterosexual cases, the epidemic displayed a relevant geographic spread, mainly from 1988-1990 to 1991-1993. Among female cases in the final time period, a cluster of high incidence rates was found towards the northwest, including very poor areas. Among pediatric cases in 1991-1996, a significant correlation was found between AIDS incidence rates and poverty levels in the respective municipal districts. The results suggest that a more complete understanding of AIDS spatial-temporal dynamics can make a major contribution to preventive measures.

Acquired Immunodeficiency Syndrome↗
PubMed2004

Prediction and setup of phytoplankton statistical model of Qiandaohu Lake.

This research considers the mathematical relationship between concentration of Chla and seven environmental factors, i.e. Lake water temperature (T), Secci-depth (SD), pH, DO, CODMn, Total Nitrogen (TN), Total Phosphorus (TP). Stepwise linear regression of 1997 to 1999 monitoring data at each sampling point of Qiandaohu Lake yielded the multivariate regression models presented in this paper. The concentration of Chla as simulation for the year 2000 by the regression model was similar to the observed value. The suggested mathematical relationship could be used to predict changes in the lakewater environment at any point in time. The results showed that SD, TP and pH were the most significant factors affecting Chla concentration.

China↗
PubMed2006

Failure of sewage pumps: statistical modelling and impact assessment.

Sewage pumping stations are directly responsible for affecting performance, i.e. failing pumps may result in combined sewer overflows or flooding. However, failures of sewage pumps are not yet incorporated in sewer assessments due to lack of knowledge and data. This paper presents the analysis of pump failure data provided by two sewer management authorities in The Netherlands. Pump failures have been studied accounting for the nature of the failures, the operation and maintenance procedures of the management authority, the ageing of the pumps and the changes in the environment of pumps. The analysis shows that sewage pumps fail relatively often due to the composition of sewage and the discontinuous operation of the pumps. The interarrival time and the duration of failures are highly variable and independent of the pump type and the specific function of the pump. The results also indicate that the serviceability of sewer systems is significantly affected by failing pumps. As a consequence, part of the environmental damage due to CSOs (combined sewer overflows) can be avoided by improving maintenance of pumping stations.

Drainage, Sanitary↗
PubMed2001

Statistical modelling of the formulation variables in non-viral gene delivery systems.

Traditionally, optimisation of a gene delivery formulation utilises a study design that involves altering only one formulation variable at any one time whilst keeping the other variables constant. As gene delivery formulations become more complex, e.g. to include multiple cellular and sub-cellular targeting elements, there will be an increasing requirement to generate and analyse data more efficiently and allow examination of the interaction between variables. This study aims to demonstrate the utility of multifactorial design, specifically a Central Composite Design, in modelling the responses size, zeta potential and in vitro transfection efficiency of some prototypic non-viral gene delivery vectors. i.e. cationic liposome-pDNA complexes, and extending the application of the design strategy to more complex vectors, i.e. tri-component lipid:polycation:DNA (LPD). The modelled predictions of how the above responses change as a function of formulation show consistency with an extensive literature base of data obtained using more traditional approaches, and highlight the robustness and utility of the Central Composite Design in examining key formulation variables in non-viral gene delivery systems. The approach should be further developed to maximise the predictive impact of data across the full range of pharmaceutical sciences.

Cells, Cultured↗
PubMedDate not supplied

Characterization and statistical modeling of bacterial (Escherichia coli) outflows from watersheds that discharge into southern Lake Michigan.

Two watersheds in northwestern Indiana were selected for detailed monitoring of bacterially contaminated discharges (Escherichia coli) into Lake Michigan. A large watershed that drains an urbanized area with treatment plants that release raw sewage during storms discharges into Lake Michigan at the outlet of Burns Ditch. A small watershed drains part of the Great Marsh, a wetland complex that has been disrupted by ditching and limited residential development, at the outlet of Derby Ditch. Monitoring at the outlet of Burns Ditch in 1999 and 2000 indicated that E. coli concentrations vary over two orders of magnitude during storms. During one storm, sewage overflows caused concentrations to increase to more than 10,000 cfu/100 mL for several hours. Monitoring at Derby Ditch from 1997 to 2000 also indicated that E. coli concentrations increase during storms with the highest concentrations generally occurring during rising streamflow. Multiple regression analysis indicated that 60% of the variability in measured outflows of E. coli from Derby Ditch (n = 88) could be accounted for by a model that utilizes continuously measured rainfall, stream discharge, soil temperature and depth to water table in the Great Marsh. A similar analysis indicated that 90% of the variability in measured E. coli concentrations at the outlet of Burns Ditch (n = 43) during storms could be accounted for by a combination of continuously measured water-quality variables including nitrate and ammonium. These models, which utilize data that can be collected on a real-time basis, could form part of an Early Warning System for predicting beach closures.

Ammonia↗
PubMed2004

Comparison of machine learning techniques with classical statistical models in predicting health outcomes.

Several machine learning techniques (multilayer and single layer perceptron, logistic regression, least square linear separation and support vector machines) are applied to calculate the risk of death from two biomedical data sets, one from patient care records, and another from a population survey. Each dataset contained multiple sources of information: history of related symptoms and other illnesses, physical examination findings, laboratory tests, medications (patient records dataset), health attitudes, and disabilities in activities of daily living (survey dataset). Each technique showed very good mortality prediction in the acute patients data sample (AUC up to 0.89) and fair prediction accuracy for six year mortality (AUC from 0.70 to 0.76) in individuals from epidemiological database surveys. The results suggest that the nature of data is of primary importance rather than the learning technique. However, the consistently superior performance of the artificial neural network (multi-layer perceptron) indicates that nonlinear relationships (which cannot be discerned by linear separation techniques) can provide additional improvement in correctly predicting health outcomes.

Aged↗