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PubMed2002

The prognostic value of several periodontal factors measured as radiographic bone level variation: a 10-year retrospective multilevel analysis of treated and maintained periodontal patients.

BACKGROUND: Assigning a prognosis to a periodontal patient is one of the greatest challenges in clinical practice. Many different factors can affect the result of periodontal therapy. The purpose of this study was to evaluate the prognostic value of some clinical, genetic, and radiographic variables in predicting bone level variation in periodontal patients (aged 40 to 60) treated and maintained for 10 years. METHODS: Sixty consecutive non-smoking patients (mean age 46.77 +/- 4.96) with moderate to severe chronic periodontitis were treated with scaling and root planing (SRP). Some patients also underwent additional surgical treatments. All patients were maintained in the same private practice for 10 years. At baseline (T0) and at least 10 years later (T2), the following clinical variables were evaluated: probing depth (PD), tooth mobility (TM), presence of prosthetic restorations (PR), and molar teeth (MT). In addition, radiographic measurements were taken of the mesial and distal distances from the cemento-enamel junction (CEJ) to the bottom of the defect (BD), to the bone crest (BC), and to the root apex (RA). At T2, a genetic test to determine the IL-1 genotype and genetic susceptibility for severe periodontal disease was performed for all 60 patients. Based on the results of this assay, the patients were categorized as IL-1 genotype positive (G+) or negative (G-). The differences between the bone levels measured at T0 and T2 (ABD), indicating the bone level variation, was used as the outcome variable. Different predictor variables were then tested using a 3-level statistical model (multilevel statistical analysis; patient, tooth, and site level). At the patient level these were: age, gender, and interaction between mean bone loss and the IL-1 genotype (mean CEJ-BD(T0) x IL-1 genotype). At the tooth level the variables were: TM(T0), PR(T0), MT(T0); and at the site level the evaluated factors were: the infrabony component of the defect (CEJ-BD(T0) - CEJ-BC(T0), PD(T0), bone level (CEJ-BD(T0)), and the residual supporting bone (BD-RA(T0)). RESULTS: Among the considered predictor parameters, the following were significantly associated with the outcome variable: 1) mean CEJ-BD(T0) x IL-1 genotype (P = 0.0019); 2) TM(T0) (P < 0.0000); 3) CEJ-BD(T0) (P < 0.0000); 4) CEJ-BD(T0) - CEJ-BC(T0) (P < 0.0000); 5) PD(T0) (P = 0.0010). Deeper probing depths at a site and tooth mobility at baseline were associated with worst prognosis. Greater CEJ-BD(T0) distance and infrabony component at a site at baseline were associated with a better prognosis. The interaction between mean CEJ-BD measurement at baseline and IL-1 genotype was significantly associated both with a good or a poor prognosis. The other parameters evaluated - age, gender, presence of molars and prosthetic restorations, and residual supporting bone - were not significantly associated with bone level variation. CONCLUSIONS: Within the scope of this study design, many traditional prognostic factors were ineffective in predicting future bone level variation and therefore were of no prognostic value. Conversely, a few specific factors at each level emerged as valuable prognostic factors. At the patient level, the prognostic factor was initial mean bone level in conjunction with a positive IL-1 genotype. At the tooth level, the prognostic factor was tooth mobility. At the site level, the significant prognostic factors were initial bone level at a site, the infrabony component of a defect, and initial probing depth at a site. The use of these factors may be of value to clinicians as predictors of bone level variation when assigning a prognosis to a patient, a tooth, or a site.

Adult↗
PubMed1995

Administrative databases, case-mix adjustments and hospital resource use: the appropriateness of controlling patient characteristics.

Hospital administrative databases are used for studying resource use and medical outcomes. The ability to use administrative data to make comparisons among providers requires accurate adjustment of rates based on case mix. Adjustment for case mix often incorporates pre-existing patient conditions such as comorbidities. We tested the hypothesis that some circulatory comorbidities can appear positively or negatively associated with percutaneous transluminal coronary angioplasty (PTCA), not for clinical reasons, but because of the population used for modeling. When statistical models included all discharges with a principal or primary diagnosis of coronary atherosclerosis, or angina with coronary atherosclerosis, multivariate analysis revealed that discharges with dysrhythmias and the more severely ill were less likely to receive PTCA. However, when analysis excluded discharges treated with options (e.g. bypass) reserved for patients with more severe conditions, the presence of dysrhythmias and more severe illness increased the odds of receiving PTCA. Variability involving the direction of association between patient characteristics and a specific intervention illustrates that rates adjusted for patient characteristics cannot be properly interpreted without a clear understanding of the rationale underlying strategies for case-mix adjustment.

Adolescent↗
PubMed2003

A statistical approach designed for finding mathematically defined repeats in shotgun data and determining the length distribution of clone-inserts.

The large amount of repeats, especially high copy repeats, in the genomes of higher animals and plants makes whole genome assembly (WGA) quite difficult. In order to solve this problem, we tried to identify repeats and mask them prior to assembly even at the stage of genome survey. It is known that repeats of different copy number have different probabilities of appearance in shotgun data, so based on this principle, we constructed a statistical model and inferred criteria for mathematically defined repeats (MDRs) at different shotgun coverages. According to these criteria, we developed software MDRmasker to identify and mask MDRs in shotgun data. With repeats masked prior to assembly, the speed of assembly was increased with lower error probability. In addition, clone-insert size affect the accuracy of repeat assembly and scaffold construction, we also designed length distribution of clone-inserts using our model. In our simulated genomes of human and rice, the length distribution of repeats is different, so their optimal length distributions of clone-inserts were not the same. Thus with optimal length distribution of clone-inserts, a given genome could be assembled better at lower coverage.

Animals↗
PubMedDate not supplied

Exposure misclassification due to residential mobility during pregnancy in epidemiologic investigations of congenital malformations.

This study addressed the question of how maternal migration between conception and birth affects estimates of risk in studies of congenital malformations when movement is related to the exposure. For example, in studying the potential association between proximity to a chemical waste site and the occurrence of birth defects, incorrect inferences might be drawn if maternal residence at birth was used as a surrogate for exposure at conception in the case when a significant amount of media attention influenced some women to move away from the site after becoming pregnant. A simple statistical model is proposed that defines the distance to a fixed exposure point measured at birth as a function of the distance to the point measured at conception, the probability of movement, the direction of movement, and the distance moved. Bias is the difference between the expected results when distance is measured at birth versus conception. The amount of bias can be substantial for movement patterns that may be likely to occur. This simplified model was used in an effort to explore and better understand the relationships between maternal migration and risk.

Abnormalities, Drug-Induced↗
PubMed1994

Bose condensation of nuclei in heavy ion collisions.

Using a fully self-consistent quantum statistical model, we demonstrate the possibility of Bose condensation of nuclei in heavy ion collisions. The most favorable conditions of high densities and low temperatures are usually associated with astrophysical processes and may be difficult to achieve in heavy ion collisions. Nonetheless, some suggestions for the possible experimental verification of the existence of this phenomenon are made.

Elementary Particles↗
PubMedDate not supplied

Will the real discrepant learning disability please stand up?

Willson and Reynolds (in this issue) challenged my thesis that the regression-based discrepancy method (RDM) is not a valid tool to detect aptitude-achievement discrepancies. In this response, I show that the statistical and theoretical counterarguments of Willson and Reynolds are based on a misreading of the statistical models presented. Furthermore, I demonstrate that the regression adjustment, which is largest for lower correlations, is the direct source of the lack of validity of the RDM procedure. Nevertheless, RDM can be considered a valid method to measure an achievement component that is unrelated to intelligence.

Child↗
PubMed2006

Systematic review of prognostic models in traumatic brain injury.

BACKGROUND: Traumatic brain injury (TBI) is a leading cause of death and disability world-wide. The ability to accurately predict patient outcome after TBI has an important role in clinical practice and research. Prognostic models are statistical models that combine two or more items of patient data to predict clinical outcome. They may improve predictions in TBI patients. Multiple prognostic models for TBI have accumulated for decades but none of them is widely used in clinical practice. The objective of this systematic review is to critically assess existing prognostic models for TBI METHODS: Studies that combine at least two variables to predict any outcome in patients with TBI were searched in PUBMED and EMBASE. Two reviewers independently examined titles, abstracts and assessed whether each met the pre-defined inclusion criteria. RESULTS: A total of 53 reports including 102 models were identified. Almost half (47%) were derived from adult patients. Three quarters of the models included less than 500 patients. Most of the models (93%) were from high income countries populations. Logistic regression was the most common analytical strategy to derived models (47%). In relation to the quality of the derivation models (n:66), only 15% reported less than 10% pf loss to follow-up, 68% did not justify the rationale to include the predictors, 11% conducted an external validation and only 19% of the logistic models presented the results in a clinically user-friendly way CONCLUSION: Prognostic models are frequently published but they are developed from small samples of patients, their methodological quality is poor and they are rarely validated on external populations. Furthermore, they are not clinically practical as they are not presented to physicians in a user-friendly way. Finally because only a few are developed using populations from low and middle income countries, where most of trauma occurs, the generalizability to these setting is limited.

Brain Injuries↗
PubMed1988

Assessment and presentation of survival experience in the Danish Breast Cancer Cooperative Group.

The purpose of this article is to describe some statistical methods usually applied in articles concerning survival data. Some fundamental concepts for survival data will be described and among others a short review of the statistical theory of Kaplan-Meier plot and log-rank test will be given. The theory will be exemplified using DBCG data with examples of increasing complexity of the statistical models. As an advanced statistical model Cox's regression model for survival data is discussed. This model has been applied in a DBCG article concerning histological malignancy grading of invasive ductal breast carcinoma and the results from this will be brought up to date and discussed.

Actuarial Analysis↗
PubMed1999

AppleTree: a multinomial processing tree modeling program for Macintosh computers.

Multinomial processing tree (MPT) models are statistical models that allow for the prediction of categorical frequency data by sets of unobservable (cognitive) states. In MPT models, the probability that an event belongs to a certain category is a sum of products of state probabilities. AppleTree is a computer program for Macintosh for testing user-defined MPT models. It can fit model parameters to empirical frequency data, provide confidence intervals for the parameters, generate tree graphs for the models, and perform identifiability checks. In this article, the algorithms used by AppleTree and the handling of the program are described.

Cognition↗
PubMed2002

Probability models and the applicability of statistical procedures in the identification of chromosomal fragile sites.

Böhm et al. (1995, Human Genetics 95, 249-256) introduced a statistical model (named FSM--fragile site model) specifically designed for the identification of fragile sites from chromosomal breakage data. In response to claims to the contrary (Hou et al., 1999, Human Genetics 104, 350-355; Hou et al., 2001, Biometrics 57, 435-440), we show how the FSM model is correctly modified for application under the assumption that the probability of random breakage is proportional to chromosomal band length and how the purportedly alternative procedures proposed by Hou, Chang, and Tai (1999, 2001) are variations of the correctly modified FSM algorithm. With the exception of the test statistic employed, the procedure described by Hou et al. (1999) is shown to be functionally identical to the correctly modified FSM and the application of an incorrectly modified FSM is shown to invalidate all of the comparisons of FSM to the alternatives proposed by Hou et al. (1999, 2001). Last, we discuss the statistical implications of the methodological variations proposed by Hou et al. (2001) and emphasize the logical and statistical necessity for fragile site identifications to be based on data from single individuals.

Algorithms↗
PubMed2004

The distance from the skin to the subarachnoid space can be predicted in premature and former-premature infants.

PURPOSE: Spinal anesthesia can be technically challenging in young infants. We studied whether the distance between the skin and the lumbar subarachnoid space in premature and former-premature young infants could be predicted prior to lumbar puncture. METHODS: The distance from skin entry point to tip of the spinal needle was measured using a caliper after lumbar spinal anesthesia at the L4-5 interspace. This distance was correlated to the patient's weight, postconceptual age and lumbar ultrasonographic measurement of the skin-to-subarachnoid space and predictive statistical models were sought. RESULTS: Thirty-five premature or former-premature infants were studied. Three models were examined: all three independent variables, weight and postconceptual age only, and weight only. The model selected contained the weight and postconceptual age, because it had the highest value for adjusted R squared, as well as the lowest value for the mean squared error. Adding the ultrasonic measurement to the model worsened the results. The statistical model that described the depth of the subarachnoid space at the L4-5 level was Y = 13.19 + 0.0026 x W - 0.12 x PCA, where Y is the distance (mm) from the skin to the subarachnoid space, W is the patient's weight (g) and PCA is the postconceptual age (weeks). Adjusted R squared was 0.72, mean square error was 2.63 and P < 10(-9). CONCLUSION: The distance between the skin and the subarachnoid space at the level of L4-5 interspace can be predicted using a statistical model based on the infant's weight and postconceptual age. Spinal ultrasound has no value in L4-5 subarachnoid space depth prediction.

Anesthesia, Spinal↗
PubMed2005

Statistical encoding model for a primary motor cortical brain-machine interface.

A number of studies of the motor system suggest that the majority of primary motor cortical neurons represent simple movement-related kinematic and dynamic quantities in their time-varying activity patterns. An example of such an encoding relationship is the cosine tuning of firing rate with respect to the direction of hand motion. We present a systematic development of statistical encoding models for movement-related motor neurons using multielectrode array recordings during a two-dimensional (2-D) continuous pursuit-tracking task. Our approach avoids massive averaging of responses by utilizing 2-D normalized occupancy plots, cascaded linear-nonlinear (LN) system models and a method for describing variability in discrete random systems. We found that the expected firing rate of most movement-related motor neurons is related to the kinematic values by a linear transformation, with a significant nonlinear distortion in about 1/3 of the neurons. The measured variability of the neural responses is markedly non-Poisson in many neurons and is well captured by a "normalized-Gaussian" statistical model that is defined and introduced here. The statistical model is seamlessly integrated into a nearly-optimal recursive method for decoding movement from neural responses based on a Sequential Monte Carlo filter.

Algorithms↗
PubMed2006

Towards a noninvasive method for determination of patient-specific wall strength distribution in abdominal aortic aneurysms.

The spatial distributions of both wall stress and wall strength are required to accurately evaluate the rupture potential for an individual abdominal aortic aneurysm (AAA). The purpose of this study was to develop a statistical model to non-invasively estimate the distribution of AAA wall strength. Seven parameters--namely age, gender, family history of AAA, smoking status, AAA size, local diameter, and local intraluminal thrombus (ILT) thickness--were either directly measured or recorded from the patients hospital chart. Wall strength values corresponding to these predictor variables were calculated from the tensile testing of surgically procured AAA wall specimens. Backwards-stepwise regression techniques were used to identify and eliminate insignificant predictors for wall strength. Linear mixed-effects modeling was used to derive a final statistical model for AAA wall strength, from which 95% confidence intervals on the model parameters were formed. The final statistical model for AAA wall strength consisted of the following variables: sex, family history, ILT thickness, and normalized transverse diameter. Demonstrative application of the model revealed a unique, complex wall strength distribution, with strength values ranging from 56 N/cm2 to 133 N/cm2. A four-parameter statistical model for the noninvasive estimation of patient-specific AAA wall strength distribution has been successfully developed. The currently developed model represents a first attempt towards the noninvasive assessment of AAA wall strength. Coupling this model with our stress analysis technique may provide a more accurate means to estimate patient-specific rupture potential of AAA.

Aging↗
PubMed2004

[Statistic-mapping modeling of rheumatic diseases prevalence among population of different regions of Russia].

AIM: To assess feasibility of the method of mapping modeling for spacial featuring of rheumatic diseases (RD) morbidity statistics for population of Russia. MATERIAL AND METHODS: Population morbidity statistics for 78 administrative units of the Russian Federation (1993-2000) were processed. Statistic-mapping modeling employed weighted mean interpolation i.e. mathematic prediction of the sign value depending on its magnitude in basic points. RESULTS: Mapped images of RD prevalence, prevalence/primary morbidity values and number of documented cases/number of rheumatologists demonstrate special distribution of the signs studied about the RF territory. The estimation of mean, minimal, maximal values and dispersion of each sign established dynamic trends in the markers for the period under study(increased area with RD prevalence above 95/1000 from 15.9 to 44.5%; increased mean ratio prevalence/morbidity from 2.8 to 3.2 and decreased ratio of patients/rheumatologists from 10882 to 9031). Regions with minimal and maximal values are shown. CONCLUSION: Statistic-mapping modeling demonstrates spacial distribution of the markers, reveals the relations between them.

Humans↗
PubMed2003

Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration.

In this paper, we show how the concept of statistical deformation models (SDMs) can be used for the construction of average models of the anatomy and their variability. SDMs are built by performing a statistical analysis of the deformations required to map anatomical features in one subject into the corresponding features in another subject. The concept of SDMs is similar to statistical shape models (SSMs) which capture statistical information about shapes across a population, but offers several advantages over SSMs. First, SDMs can be constructed directly from images such as three-dimensional (3-D) magnetic resonance (MR) or computer tomography volumes without the need for segmentation which is usually a prerequisite for the construction of SSMs. Instead, a nonrigid registration algorithm based on free-form deformations and normalized mutual information is used to compute the deformations required to establish dense correspondences between the reference subject and the subjects in the population class under investigation. Second, SDMs allow the construction of an atlas of the average anatomy as well as its variability across a population of subjects. Finally, SDMs take the 3-D nature of the underlying anatomy into account by analysing dense 3-D deformation fields rather than only information about the surface shape of anatomical structures. We show results for the construction of anatomical models of the brain from the MR images of 25 different subjects. The correspondences obtained by the nonrigid registration are evaluated using anatomical landmark locations and show an average error of 1.40 mm at these anatomical landmark positions. We also demonstrate that SDMs can be constructed so as to minimize the bias toward the chosen reference subject.

Algorithms↗
PubMed2001

Graphical representation of a generalized linear model-based statistical test estimating the fit of the single-hit Poisson model to limiting dilution assays.

Standardized statistical and graphical methods for analysis of limiting dilution assays are highly desirable to enable investigators to compare and interpret results and conclusions with greater accuracy and precision. According to these requirements, we present in this work a powerful statistical slope test that estimates the fit of the single-hit Poisson model to limiting dilution experiments. This method is readily amenable to a graphical representation. This slope test is obtained by modeling limiting dilution data according to a linear log-log regression model, which is a generalized linear model specially designed for modeling binary data. The result of the statistical slope test can then be graphed to visualize whether the data are compatible or not with the single-hit Poisson model. We demonstrate this statistical test and its graphical representation by using two examples: a real limiting dilution experiment evaluating the growth frequency of IL-2-responsive tumor-infiltrating T cells in a malignant lymph node involved by a B cell non-Hodgkin's lymphoma, and a simulation of a limiting dilution assay corresponding to a theoretical non-single-hit Poisson model, suppressor two-target Poisson model.

Allergy and Immunology↗