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At least 1,135 records · Page 63Linked to original sources
PubMed2005

The effect of time to peak ankle torque on balance stability boundary: experimental validation of a biomechanical model.

Pai and Patton (1997), using a biomechanical model, determined a set of feasible center of mass (CM) velocity-position combinations (balance stability boundary) that guarantee upright stability. In their study, the magnitude of the restoring ankle torque was used to study the subject's ability to recover balance. Recent studies have suggested that the ability to maintain a stable posture depends not only on the magnitude of the restoring torque but also on the time to generate this torque. The objectives of the present study were: (1) to build a biomechanical model that predicts the balance stability boundary which includes time to peak ankle torque, (2) to determine the capability of the model to predict successful and failed experimental balance recovery trials, and (3) to compare the predictive capability of the biomechanical model with that of a statistical model (logistic regression). A single-link-plus-foot biomechanical model was used to determine a set of balance stability boundaries, computed from the combination of maximum CM velocity and related CM position, for various times to peak ankle torque. An experiment was conducted to validate the biomechanical model. The participants self-initiated a forward destabilization and were asked to regain balance using an ankle feet-in-place strategy. Also, a forward stepwise logistic regression (predictors: CM position and velocity and time to peak ankle torque) was used to discriminate between successful and failed experimental trials. (1) The outcomes of the biomechanical model confirmed that the time to peak ankle torque drastically constrained the stability boundaries. (2) The biomechanical model predicted 79.9% of the failed experimental trials and 74.5% of the successful experimental trials. (3) The stepwise logistic regression included all independent variables and predicted 57.2% of the failed and 93.7% of the successful experimental trials. Hence, the biomechanical model showed better predictive capability than the statistical model for identifying unsuccessful balance recovery. It is noteworthy that the balance stability boundaries constrained by the speed of ankle torque development predicted the outcome of the experimental trial earlier in the time series than balance stability boundary constrained by constant ankle torque. Overall, the present biomechanical model may serve as an assessment tool to develop specific interventions towards improving a patient's speed of ankle torque development and to possibly reduce falling frequency.

Accidental Falls↗
PubMed2006

Neuron firing in driven nonlinear integrate-and-fire models.

Statistical properties of neuron firing are studied in the framework of a nonlinear leaky integrate-and-fire model that is driven by a slow periodic subthreshold signal. The firing events are characterized by first passage time densities. The experimentally better accessible interspike interval density generally depends on the sojourn times in a refractory state of the neuron. This aspect is not part of the integrate-and-fire model and must be modelled additionally. For a large class of refractory dynamics, a general expression for the interspike interval density is given and further evaluated for the two cases with an instantaneous resetting (i.e. no refractory state) and a refractory state possessing a deterministic lifetime. First passage time densities and interspike interval densities following from the proposed theory compare favorably with precise numerical simulations.

Action Potentials↗
PubMed2005

Model uncertainty and risk estimation for experimental studies of quantal responses.

Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fitting the data and may result in wide ranging estimates of risk. Bayesian model averaging (BMA) offers a strategy for addressing uncertainty in the selection of statistical models when generating risk estimates. This strategy is illustrated with two examples: applying the multistage model to cancer responses and a second example where different quantal models are fit to kidney lesion data. BMA provides excess risk estimates or benchmark dose estimates that reflects model uncertainty.

Animals↗
PubMed2004

A comparative study of an accelerated life-test model and a toxicokinetics-based model for the analysis of Porcellio scaber survival data.

Statistical models have long been used for reliability analysis and risk assessment. In the present study, an accelerated life-test model was used to analyze a set of dose-time-response data obtained with the terrestrial isopod Porcellio scaber. Survival data were experimentally obtained by exposing P. scaber to diazinon (a nonpersistent insecticide) at six concentrations between 2 and 11.31 microg/g (toxicant/soil). Survival data are presented on a weekly basis. The accelerated life-test model assumed a log-normal distribution and constant variance across all diazinon concentrations. Model parameters were obtained by maximum likelihood estimation. The accelerated life-test model was compared to a toxicokinetics-based model reported in the literature. Survival predictions made by both models were compared with the observed data. Both the accelerated life-test model and the toxicokinetics-based model underestimated toxicity at a diazinon concentration of 8 microg/g. Overall, however, the accelerated life-test model outperformed the toxicokinetics-based model, with survival predictions closer to the observed data in most cases and a stronger correlation between predicted and observed survivals. However, as a statistical model, the accelerated life-test model did not reveal mechanistic information, and only statistical and distributional interpretations of its model parameters could be made.

Animals↗
PubMed1995

Selection, mutations and codon usage in a bacterial model.

We present a statistical model of bacterial evolution based on the coupling between codon usage and tRNA abundance. Such a model interprets this aspect of the evolutionary process as a balance between the codon homogenization effect due to mutation process and the improvement of the translation phase due to natural selection. We develop a thermodynamical description of the asymptotic state of the model. The analysis of naturally occurring sequences shows that the effect of natural selection on codon bias affects genes whose products are largely required at maximal growth rate conditions or undergo rapid transient increases.

Bacteria↗
PubMed2002

Predicting outcome after acute and subacute stroke: development and validation of new prognostic models.

BACKGROUND AND PURPOSE: Statistical models to predict the outcome of patients with acute and subacute stroke could have several uses, but no adequate models exist. We therefore developed and validated new models. METHODS: Regression models to predict survival to 30 days after stroke and survival in a nondisabled state at 6 months were produced with the use of established guidelines on 530 patients from a stroke incidence study. Three models were produced for each outcome with progressively more detailed sets of predictor variables collected within 30 days of stroke onset. The models were externally validated and compared on 2 independent cohorts of stroke patients (538 and 1330 patients) by calculating the area under receiver operating characteristic curves (AUC) and by plotting calibration graphs. RESULTS: Models that included only 6 simple variables (age, living alone, independence in activities of daily living before the stroke, the verbal component of the Glasgow Coma Scale, arm power, ability to walk) generally performed as well as more complex models in both validation cohorts (AUC 0.84 to 0.88). They had good calibration but were overoptimistic in patients with the highest predicted probabilities of being independent. There were no differences in AUCs between patients seen within 48 hours of stroke onset and those seen later; between ischemic and hemorrhagic strokes; and between those with and without a previous stroke. CONCLUSIONS: The simple models performed well enough to be used for epidemiological purposes such as stratification in trials or correction for case mix. However, clinicians should be cautious about using these models, especially in hyperacute stroke, to influence individual patient management until they have been further evaluated. Further research is required to test whether additional information from brain imaging improves predictive accuracy.

Acute Disease↗
PubMed2003

How much variance is explained by ecologists? Additional perspectives.

A recent meta-analysis of meta-analyses by Møller and Jennions suggested that ecologists using statistical models are explaining between 2.5% and 5.42% of the variability in ecological studies. Although we agree that there is considerable variability in ecological systems that is not explained, we disagree with the approach and general conclusions of Møller and Jennions. As an alternate perspective, we explored the question: "How much ecological variation in relationships is not explained?" We did this by examining published studies in five different journals representative of the numerous sub-disciplines of ecology. We quantified the proportion of variance not explained in statistical models as the residual or random error compared to the total variation in the data set. Our results indicate that statistical models explain roughly half of the variation in variables of interest, vastly different from the 2.5%-5.42% reported by Møller and Jennions. This difference resulted largely from a different level of analysis: we considered the original study to be the appropriate level for quantifying variability while Møller and Jennions combined studies at different temporal and spatial scales and attempted to find universal single-factor relationships between ecological variables across study organisms or locations. Therefore, we believe that Møller and Jennions actually measured the universality of single factor effects across multiple ecological systems, not the amount of variability in ecological studies explained by ecologists. This study, combined with Møller and Jennions', illustrates importance of applying statistical models appropriately to assess ecological relationships.

Animals↗
PubMed2006

Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping.

This paper proposes a 3D statistical model aiming at effectively capturing statistics of high-dimensional deformation fields and then uses this prior knowledge to constrain 3D image warping. The conventional statistical shape model methods, such as the active shape model (ASM), have been very successful in modeling shape variability. However, their accuracy and effectiveness typically drop dramatically in high-dimensionality problems involving relatively small training datasets, which is customary in 3D and 4D medical imaging applications. The proposed statistical model of deformation (SMD) uses wavelet-based decompositions coupled with PCA in each wavelet band, in order to more accurately estimate the pdf of high-dimensional deformation fields, when a relatively small number of training samples are available. SMD is further used as statistical prior to regularize the deformation field in an SMD-constrained deformable registration framework. As a result, more robust registration results are obtained relative to using generic smoothness constraints on deformation fields, such as Laplacian-based regularization. In experiments, we first illustrate the performance of SMD in representing the variability of deformation fields and then evaluate the performance of the SMD-constrained registration, via comparing a hierarchical volumetric image registration algorithm, HAMMER, with its SMD-constrained version, referred to as SMD+HAMMER. This SMD-constrained deformable registration framework can potentially incorporate various registration algorithms to improve robustness and stability via statistical shape constraints.

Algorithms↗
PubMed1995

An introduction to logistic regression with an application to the analysis of language recovery following a stroke.

The aim of a statistical model is to present a simplified representation of the underlying structure in a data set by separating systematic features from random variation. Sometimes the purpose of a statistical model is to provide a simple descriptive summary of the data and sometimes it is to use the data for comparative or inferential purposes. In practice, the specification of a statistical model requires a thorough understanding of the substantive area of application, an assessment of the validity of the assumptions of the model, and an evaluation of the fit of the model to the data. In this paper, as an illustration of these aspects of the statistical modeling of data, we consider the specification, application, and interpretation of a logistic regression model for the investigation of relationships between binary response data and a collection of explanatory variables. We illustrate applications of the methodology using data from a prospective study of spontaneous language recovery following a stroke (Holland, Greenhouse, Fromm, & Swindell, 1989).

Brain↗
PubMed2002

Uses and limitations of statistical accounting for random error correlations, in the validation of dietary questionnaire assessments.

OBJECTIVE: To examine statistical models that account for correlation between random errors of different dietary assessment methods, in dietary validation studies. SETTING: In nutritional epidemiology, sub-studies on the accuracy of the dietary questionnaire measurements are used to correct for biases in relative risk estimates induced by dietary assessment errors. Generally, such validation studies are based on the comparison of questionnaire measurements (Q) with food consumption records or 24-hour diet recalls (R). In recent years, the statistical analysis of such studies has been formalized more in terms of statistical models. This made the need of crucial model assumptions more explicit. One key assumption is that random errors must be uncorrelated between measurements Q and R, as well as between replicate measurements R1 and R2 within the same individual. These assumptions may not hold in practice, however. Therefore, more complex statistical models have been proposed to validate measurements Q by simultaneous comparisons with measurements R plus a biomarker M, accounting for correlations between the random errors of Q and R. CONCLUSIONS: The more complex models accounting for random error correlations may work only for validation studies that include markers of diet based on physiological knowledge about the quantitative recovery, e.g. in urine, of specific elements such as nitrogen or potassium, or stable isotopes administered to the study subjects (e.g. the doubly labelled water method for assessment of energy expenditure). This type of marker, however, eliminates the problem of correlation of random errors between Q and R by simply taking the place of R, thus rendering complex statistical models unnecessary.

Diet↗
PubMed2002

A statistical approach to estimate O3 uptake of ponderosa pine in a mediterranean climate.

In highly polluted sites, stomatal behavior is sluggish with respect to light, vapor pressure deficit, and internal CO2 concentration (Ci) and poorly described by existing models. Statistical models were developed to estimate stomatal conductance (gs) of 40-year-old ponderosa pine at three sites differing in pollutant exposure for the purpose of calculating O3 uptake. Gs was estimated using julian day, hour of day, pre-dawn xylem potential and photosynthetic photon flux density (PPFD). The median difference between estimated and observed field gs did not exceed 10 mmol H2O m(-2) s(-1), and estimated gs within 95% confidence intervals. 03 uptake was calculated from hourly estimated gs, hourly O3 concentration, and a constant to correct for the difference in diffusivity between water vapor and 03. The simulation model TREGRO was also used to calculate the cumulative 03 uptake at all three sites. 03 uptake estimated by the statistical model was higher than that simulated by TREGRO because gas exchange rates were proportionally higher. O3 exposure and uptake were significantly correlated (r2>0.92), because O3 exposure and gs were highly correlated in both statistical and simulation models.

Air Pollutants↗
PubMed2002

Predictors of mortality in adult trauma patients: the physiologic trauma score is equivalent to the Trauma and Injury Severity Score.

BACKGROUND: Several statistical models (Trauma and Injury Severity Score [TRISS], New Injury Severity Score [NISS], and the International Classification of Disease, Ninth Revision-based Injury Severity Score [ICISS]) have been developed over the recent decades in an attempt to accurately predict outcomes in trauma patients. The anatomic portion of these models makes them difficult to use when performing a rapid initial trauma assessment. We sought to determine if a Physiologic Trauma Score, using the systemic inflammatory response syndrome (SIRS) score in combination with other commonly used indices, could accurately predict mortality in trauma. STUDY DESIGN: Prospective data were analyzed in 9,539 trauma patients evaluated at a Level I Trauma Center over a 30-month period (January 1997 to July 1999). A SIRS score (1 to 4) was calculated on admission (1 point for each: temperature > 38 degrees C or < 36 degrees C, heart rate > 90 beats per minute, respiratory rate > 20 breaths per minute, neutrophil count > 12,000 or < 4,000. SIRS score, Injury Severity Score (ISS), Revised Trauma Score (RTS), TRISS, Glasgow Coma Score, age, gender, and race were used in logistic regression models to predict trauma patients' risk of death. The area under the receiver-operating characteristic curves of sensitivity versus 1-specificity was used to assess the predictive ability of the models. RESULTS: The study cohort of 9,539 trauma patients (of which 7,602 patients had complete data for trauma score calculations) had a mean ISS of 9 +/- 9 (SD) and mean age of 37 +/- 17 years. SIRS (SIRS score > or = 2) was present in 2,165 of 7,602 patients (28.5%). In single-variable models, TRISS and ISS were most predictive of outcomes. A multiple-variable model, Physiologic Trauma Score combining SIRS score with Glasgow Coma Score and age (Hosmer-Lemenshow chi-square = 4.74) was similar to TRISS and superior to ISS in predicting mortality. The addition of ISS to this model did not significantly improve its predictive ability. CONCLUSIONS: A new statistical model (Physiologic Trauma Score), including only physiologic variables (admission SIRS score combined with Glasgow Coma Score and age) and easily calculated at the patient bedside, accurately predicts mortality in trauma patients. The predictive ability of this model is comparable to other complex models that use both anatomic and physiologic data (TRISS, ISS, and ICISS).

Adult↗
PubMed2000

Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds.

Models of human visual detection have been successfully used in computer-generated noise. For these backgrounds, which are generally statistically stationary, model performance can be readily calculated by computing the index of detectability d' from the noise power spectrum, the signal profile, and the model template. However, model observers are ultimately needed in more real backgrounds, which may be statistically non-stationary. We investigated different methods to calculate figures of merit for model observers in real backgrounds based on different assumptions about image stationarity. We computed performance of the nonpre-whitening matched-filter observer with an eye filter on mammography and coronary angiography for an additive or a multiplicative signal. Performance was measured either by applying the model template to the images or by computing closed-form expressions with various assumptions about image stationarity. Results show first that the structured backgrounds investigated cannot be considered stationary. Second, traditional closed-form expressions of detectability calculated from the noise power spectra with the assumption of background stationarity lead to erroneous estimates of model performance. Third, the most accurate way of measuring model performances is by directly applying the model template on the images or by computing a closed-form expression that does not assume image stationarity.

Choice Behavior↗
PubMed1989

Statistical methods for measuring interactions between protectors and sensitizers for freeze-thaw survival data.

A statistical model is developed to generate the survival probabilities of cells subjected to freeze-thaw treatments using various sensitizing and/or protective agents. The purpose is to determine whether different freeze-thaw protective agents act independently or whether there is an interaction between the agents. The model permits a statistical analysis of the data to yield an objective quantitative assessment of the size and nature of the interaction.

Cell Survival↗
PubMed1998

A state space model for the HIV epidemic in homosexual populations and some applications.

In this paper we have developed a state space model for the HIV epidemic in homosexual populations which have been divided into subpopulations according to sexual activity levels. In this model, the stochastic dynamic system model is the stochastic model of the HIV epidemic in terms of the chain multinomial model whereas the observation model is a statistical model based on the observed AIDS incidences. This model is applied to the San Francisco homosexual population for estimating the numbers of susceptible people, infective people and AIDS cases and for estimating the probabilities of HIV transmission from infective people to susceptible people given sexual contacts. The results show that the estimated numbers of AIDS incidence trace closely the observed numbers indicating the usefulness of the model. It is observed that the estimated numbers of latent people show multimodal curves and that HIV infection takes place during the primary stage and very late stage. The results have further shown that there are significant differences between the observed AIDS incidences and the estimates by the embedded deterministic model. These results indicate that using the embedded deterministic model to estimate the HIV-infected people and to predict future AIDS cases can be very misleading in some cases.

Acquired Immunodeficiency Syndrome↗
PubMed1995

Relating isobolograms to response surfaces.

The interaction index, isobologram and the appropriate contour of constant response of a dose response surface each offer essentially equivalent information regarding departures from additivity in a chemical combination. The benefit of relating the interaction index and isobolograms to a contour of a fitted dose response surface is that departures from additivity can be related to parameters of a statistical model. This permits an assessment of the statistical significance of the parameters and conclusions regarding the nature of departures from additivity which account for the variability inherent in the experiments used to generate the data. Relationships between statistical models and experimental designs can be exploited to yield economical designs for studying chemical combinations.

Data Interpretation, Statistical↗
PubMed2000

Clinical governance and the vascular surgeon.

BACKGROUND: Audit of adverse outcome might allow identification of substandard surgical results. To test this hypothesis statistical modelling was applied to two indicator vascular procedures (elective abdominal aortic aneurysm repair and carotid endarterectomy) with accepted adverse event rates. METHODS: Binomial statistical models for varying adverse event rates were constructed. A power calculation was used in an attempt to predict the case numbers required to determine substandard results for individual surgeons and vascular units. Two scenarios were considered: first a base adverse event rate of 6 per cent and surgical practice with 9, 12 and 24 per cent morbidity rates, and second a base adverse event rate of 3 per cent and surgical practice with 6, 9 and 12 per cent morbidity rates. RESULTS: A mean of 57 elective abdominal aortic aneurysm repairs and 70 carotid endarterectomies were performed per annum. The adverse event rate for both operations was 4 per cent. Power calculations revealed that 130 patients would need to be studied to detect a surgeon with an adverse event rate twice 6 per cent and over 280 patients would be required with an adverse event rate twice 3 per cent. To gather this number of patients 2 years of unit data and between 3 and 22 years of individual data would need to be studied for a base adverse event rate of 6 per cent. A base rate of 3 per cent requires 7-47 years for an individual and 4-65 years for the unit. With a base adverse event rate of 6 per cent, detection of widely variant surgical practice (four times the morbidity rate as base) requires only 21 procedures. CONCLUSION: Statistical modelling demands assumptions about accepted adverse event rates, confidence criteria and what constitutes substandard results. Data from large numbers of patients are required even for common operations with accepted adverse event rates. These data raise serious questions as to the feasibility of performing clinical governance on the basis of morbidity and mortality event rates alone.

Aortic Aneurysm, Abdominal↗