[Use of a common statistical model for the more exact diagnosis of dyslexia (reading-spelling disability)].
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Child↗
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Results for “Models, Statistical”
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
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[A statistical model for interpreting the antibiogram].
Up to now, to interpret antibiotic susceptibility tests, the common practice has been to use: first, breakpoints without any quantitative justification, secondly, concordance curves between the different measurement techniques; these are not well adapted to the heterogeneous character of bacterial populations. We hereby propose another method: it is based on a global data analysis for each bacterial species, each antibiotic family and each measurement technique. So, we have drawn up a new model for the interpretation, both global and data-processed; it is based on qualifying classes, which are obtained and interpreted by hierarchical ascendent classification, principal components analysis, and comparison with pharmacological data. It can be used by any biologist. What is more, justified breakpoints with a numerical risk and quality control are defined. There are also some additional uses: evaluation of the effect of new antibiotics, standardization of new measurement techniques, detection of the emergence of new bacterial resistance in patients, guidance for research into unknown resistance mechanisms and characters.
Computers↗
[Experimental verification of the possibility of using a genetico-statistical model for assessment of unequilibrated populations].
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Animals↗
[Statistical model of enzyme reactions].
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Enzymes↗
[A statistical model for evaluating the activity of minor forms of pulmonary tuberculosis].
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Diagnosis, Computer-Assisted↗
A geographic gradient in small rodent density fluctuations: a statistical modelling approach.
The patterns of density dependence in Fennoscandian rodents are investigated statistically using a linear autoregressive scheme. Nineteen time series of microtine abundances along a latitudinal gradient in Fennoscandia from 60 degrees N to 69 degrees N are analysed. We provide statistical evidence that there exists a latitudinal gradient in density dependence in Fennoscandian microtines. Southern populations experience significantly stronger direct density dependence than northern populations. Delayed density dependence was significantly negative throughout the region and appeared constant across the latitudinal gradient. The populations consistently exhibit dynamics of second order throughout the region. Together, the clinal direct density dependence and constant delayed density dependence give rise to a cline in cycle period from 3 to 4.5 years. The statistical results are compared to assumptions and predictions made in previous studies on the geographic gradient in the population dynamics of these rodents. The results are in agreement with the predictions of the 'generalist predator hypothesis'.
Animals↗
Computer graphics representation of a statistical model used with computer-aided diagnosis.
A description of computer graphics of a multidimensional model that is used with computer-aided diagnosis or prognosis is presented. The model is discussed and computer graphics of the model are developed. The computer graphics are suitable as visual supplements for presenting the computer-aided diagnostic model to individuals who may be inexperienced in multivariate statistics.
Animals↗
[Statistical models with reference to their value for medical process quality assurance].
Since the revision of the KVG (Art. 58) (Health Insurance Law) in 1995, systematic scientific monitoring is laid down by statute in order to ensure quality (Health Insurance Regulations; KVV Art. 77). In addition, the statistics law of 1992 prescribes the BFS statistics (with ICD coding) (model 1). Since 1983 the "Arbeitsgemeinschaft Schweizerischer Frauenkliniken" (ASF) (The Swiss Working Group of Obstetrical and Gynecological Institutions) has been maintaining a common set of statistics which amongst other things also serves for quality assurance purposes (model 2). In 1995 a number of surgical hospitals joined together under the title "Arbeitsgemeinschaft für Qualitätssicherung in der Chirurgie" (AQC) (Swiss Surgical Quality Assurance Working Group) and now also maintain similar common statistics (model 3). In this paper the three above-mentioned models are described with regard to their suitability for process quality assurance. Whilst the BFS statistics are unsuitable for this purpose, the two other methods of data collection largely fulfil the requirements for process quality assurance by using statistical models. The largest deficiency in the ASF and AQC statistics is the lack of comprehensive geographical coverage which in contrast is provided by the BFS statistics thanks to statutory requirements. However, all three models are unsuitable for the areas of structure and outcome quality assurance. Therefore other solutions must be sought for these purposes.
Data Collection↗
Statistical models of the demand for emergency medical services in an urban area.
First- and second-order statistical regression models are presented for the Emergency Medical Services (EMS) demand in an urban area as it relates to various socioeconomic, demographic, and other characteristics of the area. Individual models are formulated for different types of medical emergencies with the city of Atlanta, GA, serving as the data base. These models are generally shown to provide excellent fits to the empirical data.
Emergency Medical Services↗
What do we mean by a statistical model?
Statisticians are too often satisfied by fitting data rather than investigating the process out of which the data arose. The assumptions on which they base their models may be quite unrealistic, and while it is true that a model should not be more complicated than necessary, neither should it be too simple. Ways of approaching several sets of data from different areas of clinical medicine are considered, and different attitudes to the purpose of modelling highlighted. The transition from smoothing data, through fitting curves, to modelling underlying processes is discussed.
Analysis of Variance↗
[Statistical models and their application in descriptive epidemiology of cancer].
There are some limitations or disadvantages of statistical methods traditionally used in descriptive epidemiology of cancer. It can not handle the true relationships of several variables under srudy, and the effectiveness of a variable may often be confounded by other variables. This paper describes two kinds of multivariable regression models frequently used in descriptive epidemiology of cancer, such as the age-period-cohort (APC) model for the analysis of cancer incidence or mortality rate and the relative survival (RSR) model for the analysis of cancer survival rate. Detailed statistical methods, model fitting, parameter estimation, etc., are presented and two examples are used for illustration using data sets of oesophageal and stomach cancers diagnosed in urban Shanghai. The advantages of multivariable regression models are able to adjust effectiveness of confounder factors, and give estimations and evaluations of adjusted relative risks for the population.
Adult↗
On the utility of statistical modelling in reproductive studies.
The usefulness of mathematical modelling is discussed, with reference to a beta-binomial model which is used to describe both the implantation and birth statistics after assisted procreation. The relatively heavy computing effort required is emphasized, and contrasted with the rather simpler calculations associated with traditional statistical methods. The advantages of deriving a concise description of the data with a few important, and easily interpretable, parameters are also discussed. Numerical data provided by an IVF clinic are used to illustrate the mathematical procedures and to provide an assessment of the success of the modelling exercise.
Female↗
Understanding tuberculosis epidemiology using structured statistical models.
Molecular epidemiological studies can provide novel insights into the transmission of infectious diseases such as tuberculosis. Typically, risk factors for transmission are identified using traditional hypothesis-driven statistical methods such as logistic regression. However, limitations become apparent in these approaches as the scope of these studies expand to include additional epidemiological and bacterial genomic data. Here we examine the use of Bayesian models to analyze tuberculosis epidemiology. We begin by exploring the use of Bayesian networks (BNs) to identify the distribution of tuberculosis patient attributes (including demographic and clinical attributes). Using existing algorithms for constructing BNs from observational data, we learned a BN from data about tuberculosis patients collected in San Francisco from 1991 to 1999. We verified that the resulting probabilistic models did in fact capture known statistical relationships. Next, we examine the use of newly introduced methods for representing and automatically constructing probabilistic models in structured domains. We use statistical relational models (SRMs) to model distributions over relational domains. SRMs are ideally suited to richly structured epidemiological data. We use a data-driven method to construct a statistical relational model directly from data stored in a relational database. The resulting model reveals the relationships between variables in the data and describes their distribution. We applied this procedure to the data on tuberculosis patients in San Francisco from 1991 to 1999, their Mycobacterium tuberculosis strains, and data on contact investigations. The resulting statistical relational model corroborated previously reported findings and revealed several novel associations. These models illustrate the potential for this approach to reveal relationships within richly structured data that may not be apparent using conventional statistical approaches. We show that Bayesian methods, in particular statistical relational models, are an important tool for understanding infectious disease epidemiology.
Adult↗
Statistical modelling of the AIDS epidemic for forecasting health care needs.
The objective of this paper is to develop statistical methods for estimating current and future numbers of individuals in different stages of the natural history of the human immunodeficiency (AIDS) virus infection and to evaluate the impact of therapeutic advances on these numbers. The approach is to extend the method of back-calculation to allow for a multistage model of natural history and to permit the hazard functions of progression from one stage to the next to depend on calendar time. Quasi-likelihood estimates of key quantities for evaluating health care needs can be obtained through iteratively reweighted least squares under weakly parametric models for the infection rate. An approach is proposed for incorporating into the analysis independent estimates of human immunodeficiency virus (HIV) prevalence obtained from epidemiologic surveys. The methods are applied to the AIDS epidemic in the United States. Short-term projections are given of both AIDS incidence and the numbers of HIV-infected AIDS-free individuals with CD4 cell depletion. The impact of therapeutic advances on these numbers is evaluated using a change-point hazard model. A number of important sources of uncertainty must be considered when interpreting the results, including uncertainties in the specified hazard functions of disease progression, in the parametric model for the infection rate, in the AIDS incidence data, in the efficacy of treatment, and in the proportions of HIV-infected individuals receiving treatment.
Acquired Immunodeficiency Syndrome↗
Statistical modelling in analysis of outcome after trauma Glasgow-Coma-Scale and Innsbruck-Coma-Scale.
AIM OF THE STUDY: The current study investigated the Glasgow-Coma-Scale (GCS) and the Innsbruck-Coma-Scale (ICS) for accuracy and reliability of prehospital prediction of non-survival. METHODS: 254 patients were scored immediately after trauma. RESULTS: Both scales equally predicted non-survival with low scores (p < 0.001). The ICS was slightly better in overall prediction of patient outcome (ICS: 84.98%; GCS: 82.68%), but more importantly, statistical analysis (logistic regression model) showed a greater distance between the median scores of survivors and non-survivors, when scored with the ICS (survival: 12; non-survival: 3) than when scored with the GCS (survival: 7; non-survival: 4). CONCLUSION: The results of the present study not only suggest that it is possible to predict mortality prior to therapy for any individual GCS and ICS coma score, but also indicated the ICS to be safer to use than the GCS because of the greater distance of the median scores for survivors and non survivors.
Adolescent↗
Different statistical models to analyze epidemiological observational longitudinal data: an example from the Amsterdam Growth and Health Study.
With the development of new statistical techniques [such as generalized estimating equations (GEE)] it became possible to analyze longitudinal epidemiological relations, using all available longitudinal data. However, there are different possibilities in modeling longitudinal relations. In this paper four possible models were compared. (1) A simple model in which the actual values of the outcome and predictor variables were related (Y(it) = beta0 + beta1X(it)...); (2) A model with a time lag between outcome and predictor variables (Y(it) = beta0 + beta1X(it-1)...); (3) A model in which not the actual values, but changes in values between different time points were related ([Y(it)-Y(it-1)] = beta0 + beta1 [X(it)-X(it-1)]...); and (4) A first-order autoregressive model in which the actual value of the outcome variable at time point t is not only related to the actual value of the predictor variable at time point t, but also to the value of the outcome variable at t-1 (Y(it) = beta0 + beta1X(it) + beta2Y(it-1) +...). In this paper the use of the possible models was discussed by means of an example with data from the Amsterdam Growth and Health Study. In this longitudinal observational study six repeated measurements were carried out over a period of 15 years on subjects with an initial age of 13 years. It can be concluded that each model reflects different parts of the longitudinal relationships and the choice for a particular model must be based on logical considerations. However, in most cases epidemiologists should use the results of different models to obtain a more accurate answer to the particular epidemiological question.
Epidemiologic Methods↗
Light scattering characteristics of aerosols at ambient and as a function of relative humidity: Part II--A comparison of measured scattering and aerosol concentrations using statistical models.
The eastern United States national parks experience some of the worst visibility conditions in the nation. To study these conditions, the Southeastern Aerosol and Visibility Study (SEAVS) was undertaken to characterize the size-dependent composition, thermodynamic properties, and optical characteristics of the ambient atmospheric particles. It is a cooperative three-year study that is sponsored by the National Park Service and the Electric Power Research Institute and its member utilities. The field portion of the study was carried out from July 15 to August 25, 1995. The study design, instrumental configuration, and estimation of aerosol types from particle measurements is presented in a companion paper. In the companion paper, we compare measurements of scattering at ambient conditions and as functions of relative humidity to theoretical predictions of scattering. In this paper, we make similar comparisons, but using statistical techniques. Statistically derived specific scattering associated with sulfates suggest that a reasonable estimate of sulfate scattering can be arrived at by assuming nominal dry specific scattering and treating the aerosols as an external mixture with ammoniation of sulfate accounted for and by the use of Tang's growth curves to predict water absorption. However, the regressions suggest that the sulfate scattering may be underestimated by about 10%. Regression coefficients on organics, to within the statistical uncertainty of the model, suggest that a reasonable estimate of organic scattering is about 4.0 m2/g. A new analysis technique is presented, which does not rely on comparing measured to model estimates of scattering to evoke an understanding of ambient aerosol growth properties, but rather relies on measurements of scattering as a function of relative humidity to develop actual estimates of f(RH) curves. The estimates of the study average f(RH) curve for sulfates compares favorably with the theoretical f(RH) curve for ammonium bisulfate, which is in turn consistent with the study average sulfate ammoniation corresponding to a molar ratio of NH4/SO4 of approximately one. The f(RH) curve for organics is not significantly different from one, suggesting that organics are weakly to nonhygroscopic.
Aerosols↗
Small nodule detectability evaluation using a generalized scan-statistic model.
In this paper is investigated the use of the scan statistic for evaluating the detectability of small nodules in medical images. The scan-statistic method is often used in applications in which random fields must be searched for abnormal local features. Several results of the detection with localization theory are reviewed and a generalization is presented using the noise nodule distribution obtained by scanning arbitrary areas. One benefit of the noise nodule model is that it enables determination of the scan-statistic distribution by using only a few image samples in a way suitable both for simulation and experimental setups. Also, based on the noise nodule model, the case of multiple targets per image is addressed and an image abnormality test using the likelihood ratio and an alternative test using multiple decision thresholds are derived. The results obtained reveal that in the case of low contrast nodules or multiple nodules the usual test strategy based on a single decision threshold underperforms compared with the alternative tests. That is a consequence of the fact that not only the contrast or the size, but also the number of suspicious nodules is a clue indicating the image abnormality. In the case of the likelihood ratio test, the multiple clues are unified in a single decision variable. Other tests that process multiple clues differently do not necessarily produce a unique ROC curve, as shown in examples using a test involving two decision thresholds. We present examples with two-dimensional time-of-flight (TOF) and non-TOF PET image sets analysed using the scan statistic for different search areas, as well as the fixed position observer.
Algorithms↗