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PubMedDate not supplied

Statistical modelling of human blood carbon dioxide partial pressure.

This paper deals with statistical modelling of blood carbon dioxide partial pressure pCO2. The clinical measurements were carried out in the Arab Centre for Heart and Special Surgery in Amman. The statistical analysis of the results obtained demonstrates that pCO2 for arterial, venous and capillary blood have histograms approaching normal ones. Moreover, the experimental data suggest that the blood pCO2 can be expressed by a linear regression model. Making use of this regression model, the blood pCO2 population can indirectly be obtained with accuracy fulfilling clinical requirements. Therefore, this approach will not only lead to less invasive methods but will also result in decreasing the cost of projected analyser for blood gas analysis. The implementation of this proposed regression equation ensures the effectiveness of the model as an indirect method for pCO2 measurements.

Arteries↗

PubMed2002

Statistical modeling of tooth mobility after treating adult periodontitis.

The healing process following periodontal surgery for advanced adult periodontitis is described. Of the various indicators, tooth mobility (TM) is considered, and its relation to surgical treatment and the time lapse from the flap surgery is quantitatively modeled by non-parametric regression. Mobility is measured by an electronic apparatus, which also automatically performs the modeling. A new statistical method for TM prediction is demonstrated, and its quality is estimated. We show that the quality at the first step of prediction is approximately 0.7. This indicates that the prediction method is able to model the effect of surgery on the healing process, although the random scattering of TM data recorded in the examined group is relatively large. The influence of periodontal surgery on TM, alone and in combination with systemic metronidazole, is quantitatively characterized in two groups of 12 patients each. In the test group, which received metronidazole, TM decreased significantly 1 week postoperatively, compared to the control group without the antibiotic. The gingival fluid flow rate (GFFR) and the percentage of spirochete morphotypes detected by darkfield microscopy exhibited a similar dependence. Significant differences in TM, GFFR and the percentage of spirochetes between the two groups were observed over a period of several weeks. Probing depths (PD) in both groups at 2 and 12 months after surgery did not reveal any category with pockets deeper than 4 mm. A gain of clinical attachment level of more than 2 mm (CAL) was observed at measurements of 16.7% and 10.6% on the test and control groups, respectively, 1 year after surgery.

Adult↗

PubMed1989

Analysis of surveillance data: a rationale for statistical tests with comments on confidence intervals and statistical models.

In the examination of differences between subgroups in surveillance data, whether through simple counting or through sophisticated statistical modelling, the comparison is not between simple random samples from two or more populations. The rationale for statistical tests rests on an appeal to a model of random permutation of demographic and disease factors for the observed population during the surveillance period. The testing evaluates chance as a possible explanation for the observed results. In the analysis of internal structure in a surveillance data set, statistical tests produce a conceptually simple result that lends itself to concise presentation and flexible interpretation. Tests limit emphasis on probabilistic manipulation and on parameter estimates. They cannot stand alone, and thus encourage descriptive presentation of observations. In contrast, statistical models and confidence intervals emphasize parameters rather than distributions and compete with the data for limited space.

Data Interpretation, Statistical↗

PubMed2005

An evaluation and comparison of three commonly used statistical models for automatic detection of outbreaks in epidemiological data of communicable diseases.

We evaluated three established statistical models for automated 'early warnings' of disease outbreaks; counted data Poisson CuSums (used in New Zealand), the England and Wales model (used in England and Wales) and SPOTv2 (used in Australia). In the evaluation we used national Swedish notification data from 1992 to 2003 on campylobacteriosis, hepatitis A and tularemia. The average sensitivity and positive predictive value for CuSums were 71 and 53%, for the England and Wales model 87 and 82% and for SPOTv2 95 and 49% respectively. The England and Wales model and the SPOTv2 model were superior to CuSums in our setting. Although, it was more difficult to rank the former two, we recommend the SPOTv2 model over the England and Wales model, mainly because of a better sensitivity. However, the impact of previous outbreaks on baseline levels was less in the England and Wales model. The CuSums model did not adjust for previous outbreaks.

Australia↗

PubMed2004

Statistical models for protein validation using tandem mass spectral data and protein amino acid sequence databases.

The purpose of this work is to develop and verify statistical models for protein identification using peptide identifications derived from the results of tandem mass spectral database searches. Recently we have presented a probabilistic model for peptide identification that uses hypergeometric distribution to approximate fragment ion matches of database peptide sequences to experimental tandem mass spectra. Here we apply statistical models to the database search results to validate protein identifications. For this we formulate the protein identification problem in terms of two independent models, two-hypothesis binomial and multinomial models, which use the hypergeometric probabilities and cross-correlation scores, respectively. Each database search result is assumed to be a probabilistic event. The Bernoulli event has two outcomes: a protein is either identified or not. The probability of identifying a protein at each Bernoulli event is determined from relative length of the protein in the database (the null hypothesis) or the hypergeometric probability scores of the protein's peptides (the alternative hypothesis). We then calculate the binomial probability that the protein will be observed a certain number of times (number of database matches to its peptides) given the size of the data set (number of spectra) and the probability of protein identification at each Bernoulli event. The ratio of the probabilities from these two hypotheses (maximum likelihood ratio) is used as a test statistic to discriminate between true and false identifications. The significance and confidence levels of protein identifications are calculated from the model distributions. The multinomial model combines the database search results and generates an observed frequency distribution of cross-correlation scores (grouped into bins) between experimental spectra and identified amino acid sequences. The frequency distribution is used to generate p-value probabilities of each score bin. The probabilities are then normalized with respect to score bins to generate normalized probabilities of all score bins. A protein identification probability is the multinomial probability of observing the given set of peptide scores. To reduce the effect of random matches, we employ a marginalized multinomial model for small values of cross-correlation scores. We demonstrate that the combination of the two independent methods provides a useful tool for protein identification from results of database search using tandem mass spectra. A receiver operating characteristic curve demonstrates the sensitivity and accuracy level of the approach. The shortcomings of the models are related to the cases when protein assignment is based on unusual peptide fragmentation patterns that dominate over the model encoded in the peptide identification process. We have implemented the approach in a program called PROT_PROBE.

Amino Acid Sequence↗

PubMed1998

A multiplicative statistical model predicts the size distribution of unruptured intracranial aneurysms.

A statistical model for characterizing the erratic nature of aneurysm evolution is developed and tested. This model is based upon a multiplicative hypothesis, whereby it is theorized that the progressive changes in the size of a given aneurysm are determined by random multipliers. Such a model would predict that within a large population of aneurysms, a lognormal histogram for aneurysm sizes would occur (i.e. the logarithms of aneurysm size would have a normal distribution). When applied to previously published clinical data of unruptured aneurysms by Crompton (1966) and McCormick et al. (1970), the model is found to adequately describe both sets of data. The methods introduced in this paper illustrate the utility of incorporating statistical and clinical insights with fundamental biometry for studying the complex phenomena of aneurysm growth and rupture.

Aneurysm, Ruptured↗

PubMed2000

A statistical model of the human core-temperature circadian rhythm.

We formulate a statistical model of the human core-temperature circadian rhythm in which the circadian signal is modeled as a van der Pol oscillator, the thermoregulatory response is represented as a first-order autoregressive process, and the evoked effect of activity is modeled with a function specific for each circadian protocol. The new model directly links differential equation-based simulation models and harmonic regression analysis methods and permits statistical analysis of both static and dynamical properties of the circadian pacemaker from experimental data. We estimate the model parameters by using numerically efficient maximum likelihood algorithms and analyze human core-temperature data from forced desynchrony, free-run, and constant-routine protocols. By representing explicitly the dynamical effects of ambient light input to the human circadian pacemaker, the new model can estimate with high precision the correct intrinsic period of this oscillator ( approximately 24 h) from both free-run and forced desynchrony studies. Although the van der Pol model approximates well the dynamical features of the circadian pacemaker, the optimal dynamical model of the human biological clock may have a harmonic structure different from that of the van der Pol oscillator.

Algorithms↗

PubMedDate not supplied

Statistical models for prediction of arterial oxygen and carbon dioxide tensions during mechanical ventilation.

The possibility of constructing statistical models for prediction of alveolar oxygen and carbon dioxide tensions has been investigated in 20 mechanically ventilated patients in acute respiratory failure (ARF). Linear multiple regression analysis using PaCO2 and PaO2 as dependent variables was used to construct (a) models for individual patients, (b) models for specific diagnostic groups and (c) general models (all patients). The coefficient of determination (R2) was highest for the individual patient models (0.38-0.99) and lowest for the general models (0.28-0.49). In order to achieve a high predictive accuracy, models matching individual patients should be constructed on the basis of initial invasive blood gas measurement. Statistically derived models may bring better understanding of the behaviour of factors influencing arterial gas tensions in ARF and may be of value in the management of patients on mechanical ventilation.

Adult↗

PubMedDate not supplied

Estimation of subject-specific normal ranges based on some statistical models of an individual's physiological variations.

Three statistical models of the individual's physiological variations, proposed by Dr Eugene K. Harris, were applied to the long-term series of individual test results in our health control system. It was found from this study that, in most cases, the homeostatic model shows the best fit to the population level from comparing correlation coefficients between the observations and estimations made based on Harris's three models. It was also found that for most cases, the homeostatic model gives the most reliable estimates in individual's level from the comparison of the chi-square values between the observations and estimations made for the individual's successive test results. Further, for a group of members with hyperglycaemia, the proportion of individuals in whom the random-walk model produced the most accurate predictions was increased to the proportion in the normal group. In the age group under 39 years of age, the autoregressive model showed a relatively high degree of predictive success, while the homeostatic model showed a relatively low degree in comparison with the results found in other age groups. From these investigations, it was found that the physiologically normal state shows strict homeostatic stability.

Female↗

PubMed2002

Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.

Algorithms↗

PubMed1993

A task-based statistical model of a worker's exposure distribution: Part II--Application to sampling strategy.

A task-based statistical model of a worker's exposure distribution for an airborne chemical toxicant is applied to estimating the long-term average exposure level, mu. The precision in estimation is represented by the variance of the sample estimator, denoted by Var[mu]. A traditional sampling strategy consists of integratively measuring the 8-hr time-weighted average exposure level on randomly selected workdays, and computing the sample mean; this strategy is termed "simple one-stage cluster sampling," where each 8-hr workday is a cluster of thirty-two 15-min periods. Three alternative strategies involving measurements of 15-min TWAs are examined: simple random sampling of 15-min periods, and stratified random sampling of 15-min periods with proportional allocation by task, and with optimum allocation by task. All four survey designs provide unbiased estimates of mu. However, for a fixed cost, the stratified sampling designs may provide a lower Var[mu] than simple one-stage cluster sampling for less work time monitored.

Air Pollutants, Occupational↗

PubMed1991

Statistical modelling in analysis of prognosis in glioblastoma multiforme: a study of clinical variables and Ki-67 index.

Statistical modelling was used to analyse clinical features and the Ki-67 proliferation index in a study of 77 cases of glioblastoma multiforme. Relative youth, frontal tumour site and treatment with external beam radiation had an important positive influence on survival. The pre-operative Karnovsky score and the presence of necrosis were not related to outcome. Despite the attractive hypothesis that rapidly proliferating tumours might be associated with a worse prognosis compared with slowly proliferating lesions, the Ki-67 index in this study offered no prognostic information even when individual sites were considered separately. The use of this form of computer modelling and its role in analysis of prognostic data is discussed.

Adult↗

PubMed2006

Living donor liver transplantation in high-risk vs. low-risk patients: optimization using statistical models.

Living donors represent a recognized alternative for facilitating the access to transplantation in a period of organ shortage. However, which candidates should be preferentially considered for living-donor liver transplantation (LDLT) is debated. The aim of this study was to create statistical models to determine which strategies of selection for LDLT provide the most efficient contribution. The study included 331 patients listed for deceased-donor transplantation (DDLT) and 128 transplanted with living donors. Statistical models predicting the events following listing were created and combined in a multistate model allowing the testing of different strategies of selection for LDLT and to compare their results. Taking 3-yr survival after listing as the principal end-point, selecting the 20% patients at highest risk of death on the waiting list gave better results than selecting the 20% patients at lowest risk of death after LDLT (70% vs. 64%, respectively). These strategies resulted in waiting list mortality rates of 17% and 8%, respectively. One-year survival after LDLT was lower in high-risk patients (85%) than in low-risk patients (91%). However, the 1-yr survival benefit derived from LDLT was 75% in high-risk patients while it was nil in low-risk patients. In conclusion, LDLT is more effective for overcoming the consequences of organ shortage when performed in patients at high risk of death on the waiting list. On an individual basis, the sickest patients are those who derive the most important benefit from LDLT. This study provides incentives for considering LDLT in high-risk patients.

Adolescent↗

PubMed2005

Analysis of the plant architecture via tree-structured statistical models: the hidden Markov tree models.

Plant architecture is the result of repetitions that occur through growth and branching processes. During plant ontogeny, changes in the morphological characteristics of plant entities are interpreted as the indirect translation of different physiological states of the meristems. Thus connected entities can exhibit either similar or very contrasted characteristics. We propose a statistical model to reveal and characterize homogeneous zones and transitions between zones within tree-structured data: the hidden Markov tree (HMT) model. This model leads to a clustering of the entities into classes sharing the same 'hidden state'. The application of the HMT model to two plant sets (apple trees and bush willows), measured at annual shoot scale, highlights ordered states defined by different morphological characteristics. The model provides a synthetic overview of state locations, pointing out homogeneous zones or ruptures. It also illustrates where within branching structures, and when during plant ontogeny, morphological changes occur. However, the labelling exhibits some patterns that cannot be described by the model parameters. Some of these limitations are addressed by two alternative HMT families.

Combretaceae↗

PubMed1991

A bibliography and comments on the use of statistical models in epidemiology in the 1980s.

This paper reviews developments in statistical modelling in epidemiology in the 1980's, with emphasis on cohort and case-control studies. The central roles of the logistic and proportional hazard models are highlighted, and it is shown how these models lead to a deeper understanding of classical designs and methods of analysis as well as to efficient new designs and analytical procedures. The important area of model misspecification is discussed, including the problems of omitted latent structure, mis-modelling of available measurements, missing data and errors in measurements. Various designs motivated by the logistic model are illustrated numerically, and designs based on the proportional hazards model are discussed, as are papers on sample size determination. There are brief introductions to the literature on other topics, including attributable risk, disease clustering, family studies and genetics, analysis of disease incidence data, infectious disease, longitudinal data, screening and miscellaneous related topics in statistics. An extensive bibliography is indexed according to the outline of the paper.

Data Interpretation, Statistical↗

PubMed1997

Relations of plasma ACTH and cortisol levels with the distribution and function of peripheral blood cells in response to a behavioral challenge in breast cancer: an empirical exploration by means of statistical modeling.

This study explores by means of statistical modeling the relations between adrenocorticotrophin hormone (ACTH) and cortisol levels and distribution and function of peripheral blood cells in response to an acute stressor consisting of a standardized speech task in breast cancer patients with axillary lymph node metastases and distant metastases. As a control group age-matched women participated in this study. The preliminary findings show that the effect of ACTH on immunoreactivity is related to the health of the doctor. In node-positive breast cancer patients and healthy women, ACTH has a modest positive effect on T lymphocyte percentages and on pokeweed-induced proliferation at baseline and in response to the speech task. In contrast, in breast cancer patients with distant metastases, ACTH has a negative effect on T lymphocyte and function at baseline and in response to the stressor. Interestingly, neither ACTH nor cortisol levels were related to natural killer (NK) cell percentages and natural killer cell activity (NKCA). In addition, it appeared that cortisol had a positive effect on CD3 cell percentages when the health of the donor was taken into account. This effect was most distinct on CD3 cells measured at baseline. If replicated on a larger scale, these findings may indicate that the hypothalamic pituitary adrenal axis plays a role in the adaptation of the host defenses in reaction to acute stress, particularly those involving T lymphocytes. Moreover, these findings may suggest that the health of the donor may be an important effect modification factor in the relations between neuroendocrines and immunoreactivity.

Journal Article↗

PubMedDate not supplied

[Calculation of the stability of mini-duplexes of DNA in an electrolyte solution using a molecular mechanics method in approximating a statistical model of the environment].

Approximation of the statistical model of environment (SME) to estimate the energy of the macromolecule in electrolyte solution has been developed and used for calculating the conformational energy of nucleic acids by means of the molecular mechanics method. Calculation of base pairs opening delta Hcalop enthalpies and enthalpies of DNA miniduplexes dissociation delta Hcaldis were performed for 10 types of diduplexes. The approximation SME enable to perform calculations of the absolute base-dependent values of delta Hcalop which coincide in the range of 1 kcal/mol with the experimental base-dependent values of the helix-coil transition enthalpies. Values of dissociation enthalpies delta Hcalop greater than delta Hcaldis for all miniduplexes, the difference of delta Hcalop--delta Hcaldis determine the base-dependent energy of the helix-coil boundary. The values of activation barriers for the strands dissociation delta H d not equal to congruent to 8 kcal/mol and association delta H not equal to as congruent to 4 kcal/mol were obtained for GG/CC and AA/TT duplexes. It is concluded that the approximation SME enables to increase substantially the accuracy of the calculations of the macromolecule conformational rearrangement enthalpies in the electrolytes solution.

DNA↗

PubMed2003

Statistical model of the interactions between Helicobacter pylori infection and gastric cancer development.

BACKGROUND: The bacterium Helicobacter pylori is associated with a number of gastrointestinal diseases, such as gastric ulcer, duodenal ulcer and gastric cancer. Several histological changes may be observed during the course of infection; some may influence the progression towards cancer. The aim of this study was to build a statistical model to discover direct interactions between H. pylori and different precancerous changes of the gastric mucosa, and in what order and to what degree those may influence the development of the intestinal type of gastric cancer. METHODS: To find direct and indirect interactions between H. pylori and different histological variables, log-linear analyses were used on a case-control study. To generate mathematically and biologically relevant statistical models, a designed algorithm and observed frequency tables were used. RESULTS: The results show that patients with H. pylori infection need to present with proliferation and intestinal metaplasia to develop gastric cancer of the intestinal type. Proliferation and intestinal metaplasia interacted with the variables atrophy and foveolar hyperplasia. Intestinal metaplasia was the only variable with direct interaction with gastric cancer. Gender had no effect on the variables examined. CONCLUSION: The direct interactions observed in the final statistical model between H. pylori, changes of the mucosa and gastric cancer strengthens and supports previous theories about the progression towards gastric cancer. The results suggest that gastric cancer of the intestinal type may develop from H. pylori infection, proliferation and intestinal metaplasia, while atrophy and foveolar hyperplasia interplay with the other histological variables in the disease process.

Aged↗