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At least 109 records · Page 6Linked to original sources
PubMed1977

Three statistical models for estimating length of stay.

The probability density functions implied by three methods of collecting data on the length of stay in an institution are derived. The expected values associated with these density functions are used to calculate unbiased estimates of the expected length of stay. Two of the methods require an assumption about the form of the underlying distribution of length of stay; the third method does not. The three methods are illustrated with hypothetical data exhibiting the Poisson distribution, and the third (distribution-independent) method is used to estimate the length of stay in a skilled nursing facility and in an intermediate care facility for patients enrolled in California's MediCal program.

California↗
PubMed1989

Statistical modelling of mitochondrial power supply.

By experiment and theory, formulae are derived to calculate the response of mitochondrial power supply, in flux and potential, to an ATP consuming enzyme load, incorporating effects of varying amounts of (i) enzyme, (ii) total circulating adenylate, and (iii) inhibition of the ATP/ADP translocase. The formulae, which apply between about 20% and 80% of maximum respiration, are the same as for the current and voltage of an electrical circuit in which a battery with potential, linear in the logarithm of the total adenylate, charges another battery whose opposing potential is also linear in the same logarithm, through three resistances. These resistances produce loss of potential due to dis-equilibrium of (i) intramitochondrial oxidative phosphorylation, (ii) the ATP/ADP translocase, and (iii) the ATP-consuming enzyme load. The model is represented geometrically by the following configuration: when potential is plotted against flux, the points lie on two pencils of lines each concurrent at zero respiration, the two pencils describing the respective characteristics of the mitochondrion and enzyme. Control coefficients and elasticities are calculated from the formulae.

Adenosine Triphosphate↗
PubMed1986

Statistical modelling of injury severity, with special reference to driver and front seat passenger in single-vehicle crashes.

First, the statistical analysis of injury severity is introduced by considering the following topics: Interpretation of the recorded grades of injury severity (e.g. fatal, serious, slight, none) as divisions of a continuous scale. The possible presence of errors in recording injury severity. How this is used in the statistical analysis of injury severity data, including discussion of computing methods. Secondly, attention is turned to data in which the severities of injury to two people in the same crash is given. British accident data for 1969-72 has been processed to give a cross-tabulation of the severity of injury to the driver and to the front seat passenger in four types of single-vehicle accidents (overturning and nonoverturning, each in rural and in urban areas). Three complications with this data are that the number of non-injury accidents is unknown, that the cases where a passenger was present but uninjured could not be distinguished from those where there was no passenger, and that there is inconsistency in the positioning of the thresholds separating serious from slight injury, and slight from no injury. A positive correlation between the severities of injury to the two occupants is evident in the data. This is interpreted as being largely due to the speed of the crash, and a model is developed in which the two severities jointly have a bivariate normal distribution.

Accidents, Traffic↗
PubMed1976

A statistical model of the dynamics of a mosquito vector (Culex tarsalis) population.

A model of the dynamics of a mosquito Culex tarsalis is derived that includes the life states through which the mosquito proceeds. Transition probabilities from one state (egg, larva, pupa and adult) to another are derived and they depend on the duration of stay and mortality in each state. A formula is derived for the expected number of mosquitoes alive at any time during the spring or summer. This formula depends on the number of eggs oviposited and the transition probabilities. Data are used to estimate the parameters and to illustrate the usefulness of this model in examining the effect of changes in mosquito survival on the dynamics of the population.

Animals↗
PubMed1989

Anaerobic threshold estimation by statistical modelling.

Anaerobic threshold (AT) is usually estimated as a change point problem by visual analysis of the cardiorespiratory response to incremental dynamic exercise. In this study, two phase linear (TPL) models of the linear-linear and linear-quadratic type were used for the estimation of AT. The correlation coefficient between the classical and statistical approaches was 0.88, and 0.89 after outlier exclusion. The TPL models provide a simple method for estimating AT that can be easily implemented using a digital computer for the automatic pattern recognition of AT.

Anaerobic Threshold↗
PubMed1991

Statistical modeling of prognostic indices for evaluation of critically ill patients.

OBJECTIVE: To identify the most predictive association of variables from the usual indices of severity of illness by statistical objective analysis. DESIGN: Logistic regression analysis of the different variables of the most important indices. SETTING: A general critical care medicine group practice in a university hospital. PATIENTS: A total of 630 critical care patients age 12 to 87 yrs were evaluated. The most important indices of severity of illness and the corresponding variables were recorded and the patient's course was followed for 3 months after ICU admission. MEASUREMENTS AND MAIN RESULTS: One of our hypotheses was that the inclusion of an excessive number of variables to obtain the most common prognostic indices of mortality in critical care patients results in an underestimation of mortality and a redundancy of prognostic information. We performed a logistic regression analysis using the variables of the currently used indices of critical care prognosis: Acute Physiology Score, Simplified Acute Physiology Score, Acute Physiology Score-II, and Mortality Prediction Model. This mathematical approach resulted in a model of five variables: organ system failure, blood glucose, serum calcium, serum prothrombin activity, and serum osmolality. The score obtained from this model gave accurate prognostic criteria:sensitivity 91.2% and specificity 90%, using a cutoff point of 0.7; sensitivity 86% and, specificity 94%, using a cutoff point of 0.5. CONCLUSIONS: Our results show that suitable statistical management of the discriminant prognostic variables allows reduction of the number of variables of the severity indices currently used, obtaining five more predictive variables.

Adolescent↗