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PubMed2003

Vital and health statistics: Russian Federation and United States, selected years 1985-2000 with an overview of Russian mortality in the 1990s.

This report provides comparative vital and health statistics data for recent years for the Russian Federation and the United States. Statistical data for Russia and from the Ministry of Health of Russia and from Goskomstat, the central statistical organization of Russia. Information for the United States comes from various data systems of the Centers for Disease Control and Prevention's (CDC) National Center for Health Statistics (NCHS) as well as other parts of the Department of Health and Human Services. The initial section of the report summarizes information on recent mortality trends in the Russian Federation. During the 1990s, Russia experienced a major increase in mortality from 1990 to 1994, a substantial reduction in mortality from 1994 to 1998, and another major increase from 1998 to 2000. The mortality overview uses tables and figures to describe mortality changes by age group, sex, and cause of death, and to determine the contribution of each of these to changes in life expectancy. The overview also considers risk factors and other issues underlying these trends, in an attempt to understand the impact of major mortality determinants on changes in life expectancy. The section on vital and health statistics uses tables, figures, and commentary to present information on many different health measures for the populations of the two countries. Topics covered include population size, fertility, life expectancy, infant mortality, death rates, communicable diseases, and various health personnel and health resource measures. The commentary includes a discussion of data quality issues that affect the accuracy and comparability of the information presented. Data are provided for selected years from 1985 to 2000. In addition to national data, mortality information on urban and rural subgroups in Russia is provided. A glossary of terms at the end of the report provides additional information on definitions and data sources and limitations.

Adolescent↗
PubMed2004

Visualization of the variability of 3D statistical shape models by animation.

Models of the 3D shape of anatomical objects and the knowledge about their statistical variability are of great benefit in many computer assisted medical applications like images analysis, therapy or surgery planning. Statistical model of shapes have successfully been applied to automate the task of image segmentation. The generation of 3D statistical shape models requires the identification of corresponding points on two shapes. This remains a difficult problem, especially for shapes of complicated topology. In order to interpret and validate variations encoded in a statistical shape model, visual inspection is of great importance. This work describes the generation and interpretation of statistical shape models of the liver and the pelvic bone.

Humans↗
PubMed1998

Technical note: the initial stages of statistical data analysis.

OBJECTIVE: To provide an overview of several important data-related considerations in the design stage of a research project and to review the levels of measurement and their relationship to the statistical technique chosen for the data analysis. BACKGROUND: When planning a study, the researcher must clearly define the research problem and narrow it down to specific, testable questions. The next steps are to identify the variables in the study, decide how to group and treat subjects, and determine how to measure, and the underlying level of measurement of, the dependent variables. Then the appropriate statistical technique can be selected for data analysis. DESCRIPTION: The four levels of measurement in increasing complexity are nominal, ordinal, interval, and ratio. Nominal data are categorical or "count" data, and the numbers are treated as labels. Ordinal data can be ranked in a meaningful order by magnitude. Interval data possess the characteristics of ordinal data and also have equal distances between levels. Ratio data have a natural zero point. Nominal and ordinal data are analyzed with nonparametric statistical techniques and interval and ratio data with parametric statistical techniques. ADVANTAGES: Understanding the four levels of measurement and when it is appropriate to use each is important in determining which statistical technique to use when analyzing data.

Journal Article↗
PubMed1992

An image analysis and statistical evaluation program for the assessment of tumour cell invasion in vitro.

Tumour cell invasion is a complex process, which is essential for the formation of metastasis and is therefore of critical clinical importance. For detailed investigations of the invasive process, quantifiable in vitro models of invasion are necessary. In this study we describe an image analysis procedure and a statistical program which facilitate an objective analysis of experiments carried out using the embryonic chick heart invasion model of Mareel. Tumour multicellular spheroids are confronted with embryonic chick heart fragments in culture and are sampled after different time intervals for up to 7 days. Immunohistological sections are then evaluated by an image analysis procedure which provides 9 parameters indicating invasion, proliferation and destruction taking place in the confrontation cultures. The data obtained by image analysis are further evaluated by a statistical program which describes the change with time of each parameter by means of linear regression analysis. Thus the data obtained at various time intervals serve as the source data for a single statistic, namely the slope of the regression line. Confidence intervals and statistical differences between various experiments can be calculated. In order to make the procedure more comprehensible in biological terms, the program provides a full text interpretation of the experimental results. The image analysis procedure in conjunction with statistical evaluation and text interpretation provides a comprehensive tool for the quantitative assessment of experimental invasion in vitro.

Animals↗
PubMed1989

The power of the Z statistic: implications for trauma research and quality assurance review.

The Z statistic can be used to test whether the observed number of survivors in a specific trauma population is significantly different from what would be expected based on the Major Trauma Outcome Study (MTOS) norms. However, as with any statistic, inferences based on the Z statistic should be made with care. This is particularly true when a non-significant Z statistic is observed. The purpose of this paper, using data from a large, urban trauma registry, is to illustrate how the power of the Z statistic, or its ability to detect a difference between observed and expected survival, is influenced by the magnitude of the difference, the direction of the difference, the survival probability distribution of the study population, and the sample size. The implications for trauma research and quality assurance review are discussed.

Craniocerebral Trauma↗
PubMed1987

Discrimination of DNA ploidy patterns by order statistics.

The use of order statistics to discriminate and classify DNA ploidy patterns is proposed, especially for the classification of additional observations: whether a given sample is more likely to have come from a normal or an abnormal tissue, and with what probability, based on its ploidy pattern. The method involves the order of observations within each of several samples (e.g., euploid and aneuploid DNA patterns) and the use of subsets of the obtained order statistics as independent variables in a linear discriminant analysis. It thus replaces univariate observations by (some of) their order statistics, which are then used as the variables in the discriminant analysis. The procedure does not require normality of distributions or the transformation of nonnormal distributions, as do many discriminant functions; order statistics are usually distribution-free and thus are particularly useful for nonparametric inference. Preliminary simulation studies verified the potential usefulness of the order statistics discriminant function method as applied to DNA ploidy analysis. Its advantages as compared to the usual methods for hypothesis testing, e.g., the use of the chi-square or Kolmogorov-Smirnov tests to as certain "goodness-of-fit," is discussed. The proposed method is easy to implement and easy to interpret; it is also applicable to the study of distributions of other types of measurements.

Biometry↗
PubMed1986

[Significance probability mapping of brain electrical activity--its problem and specified z-statistic mapping].

Significance probability mapping (SPM) of brain electrical activity first described by Duffy et al. is useful tool for studying functional aspects of brain disease. Z-statistic SPM is able to identify the area of brain electrical activity deviated with statistic significance from the control group. The problem of this method is, however, that the nature of deviation, i.e., whether it is increase or decrease of electrical activity, can not be displayed. From this point of view, we attempted to use modified z-statistic method. Statistically deviated region and its nature can be clearly displayed on the same picture by analyzing EEG with this method. This method can be applied to SPM of evoked potentials. SPM is not yet complete method for the assessment of brain electrical activity, but there is much room for adopting other statistic method that is more suitable for the aim of the study. Functional aspects of the brain will be more readily clarified by the use of modified SPM and by combination with findings of CT scan, NMR and PET that can give morphological and metabolic information.

Adult↗
PubMed1995

A desk top computer program for visualized statistical analysis of lesional images in intracerebral hemorrhage.

Statistically identified information on the relationships between the sites of lesions in intracerebral hemorrhage (ICH), risk factors such as a smoking or drinking habit, anamnesis, and biochemical data through blood tests will extend assistance to neuromedical clinicians on their daily clinical duties. It will provide them with a useful guide to determine the method of treatment. Also, it will be a basic research material for their clinical studies on diagnosis, progress, or prognosis in ICH. In order to obtain such statistics with the help of the computer, we need to have a computationally effective image database system. As is generally known, medical image data especially requires a great amount of storage; high-speed processing techniques are therefore also needed to deal with such data effectively. In addition, it is desired that we have outputs from the analysis edited with well-visualized effect, using 3D computer graphics, etc. These are why most existing image processing systems have been designed to work on comparatively large-scale computers. So far as we know, it is hard to find a practical and inexpensive personal computer-based application system for visualized statistical analysis of lesional images in ICH. We have developed a desk top computer-based program for statistical analysis of lesional image data of ICH. With this system, we can organize a medical image database that consists of the personal data of patients with ICH (sex, age, occupation, diagnosis, symptoms, part of physical disorder, etc.), risk factors, anamnesis (cerebral apoplexy, hypertension, hypotension, corpulence, diabetes, hyperlipidemia, atrial fibrillation, valvular endocarditis, etc.), biochemical data of blood, and lesional image data from CT or MRI. This system consists of the following components: 1) database management, 2) information retrieval (IR), 3) lesional image processing, 4) statistical analysis, and 5) prognostic prediction. The images are drawn manually on prescribed data sheets by tracing CT or MRI films and are read through the image scanner; then the compressed data of the digitized images is recorded in the database. Each recorded image data consists of the following two components: the frame image that corresponds to the contour of tissues of interest on the corresponding sliced section, and the actual image that corresponds to the lesion itself. In our system, these two images are separately stored and managed so that we can effectively perform subsequent image analysis. Other variables in the database (risk factors, anamnesis, etc.) are mainly used as search keys for making the aggregate of image data by the IR subsystem. In any aggregate, its elements, namely image data, have common medical background descriptions with the search keys. These aggregates can be used as input for the lesional image processing subsystem. With this subsystem, we can obtain the accumulated distribution of frequencies within a specified range of any sliced section, display planar color maps and profiles associated with the distribution, reconstruct it in 3D form, perform transformations of 3D images (zooming, enhancement, rotation, etc.), and test the significant difference of frequencies between any two different sites. We have been making practical use of this system to find the neurological relationship between the symptom (dysarthria, and paralysis of upper/lower limbs) and the site of lesion with cerebral infarction in pons. This study is quite important since the distributions of pyramidal tract related to the above symptom in pons are not well-known compared to those in cerebral cortex, internal capsule, or cerebral peduncle. With our system, we have obtained several findings expected to be helpful for this study. However, since this study is still in the initial phases, we will only present the outcome as a working example of our system. Our system was originally developed for analyzing lesional images with ICH. However, it could

Cerebral Hemorrhage↗
PubMed1996

Outcome of temporal lobe epilepsy surgery predicted by statistical parametric PET imaging.

UNLABELLED: PET is useful in the presurgical evaluation of temporal lobe epilepsy. The purpose of this retrospective study is to assess the clinical use of statistical parametric imaging in predicting surgical outcome. METHODS: Interictal 18FDG-PET scans in 17 patients with surgically-treated temporal lobe epilepsy (Group A-13 seizure-free, group B = 4 not seizure-free at 6 mo) were transformed into statistical parametric imaging, with each pixel representing a z-score value by using the mean and s.d. of count distribution in each individual patient, for both visual and quantitative analysis. RESULTS: Mean z-scores were significantly more negative in anterolateral (AL) and mesial (M) regions on the operated side than the nonoperated side in group A (AL: p < 0.00005, M: p = 0.0097), but not in group B (AL: p = 0.46, M: p = 0.08). Statistical parametric imaging correctly lateralized 16 out of 17 patients. Only the AL region, however, was significant in predicting surgical outcome (F = 29.03, p < 0.00005). Using a cut-off z-score value of -1.5, statistical parametric imaging correctly classified 92% of temporal lobes from group A and 88% of those from Group B. CONCLUSION: The preliminary results indicate that statistical parametric imaging provides both clinically useful information for lateralization in temporal lobe epilepsy and a reliable predictive indicator of clinical outcome following surgical treatment.

Adult↗
PubMed1998

Prognosis of intravesical bacillus Calmette-Guerin therapy for superficial bladder cancer by immunological urinary measurements: statistically weighted syndrome analysis.

PURPOSE: The goal of this research was to discover new biological indicators in urine which could be used for short-term prognosis of local Bacillus Calmette-Guerin (BCG) therapy outcome in patients with superficial bladder cancer. PATIENTS AND METHODS: We measured and statistically evaluated soluble immunological molecules in urine from bladder cancer patients (n = 34) receiving BCG intravesically. Urine was collected following each of 6 weekly treatments, processed and assayed. The data base included measurements of interleukin-1 (IL-2, IL-4, IL-6, IL-10, IL-12, soluble intercellular adhesion molecule-1 (sICAM-1), tumour necrosis factor-alpha (TNF alpha), soluble CD14 (sCD14), interferon-gamma (IFN gamma), GM-CSF, volume of urine and its pH. The clinical response was evaluated by urine histology and random quadrant biopsy 3 months after the start of therapy. Patients were divided into 2 groups, with good and poor therapeutic effect. The initial complete response rate was 62% (21/34). The data base was analyzed using traditional multivariate statistical methods and a pattern recognition method which deals with combinatorial-statistical analysis (statistically weighted syndromes (SWS) method) of the gradated features. The SWS method is capable of identifying robust patterns in small "fuzzy" sets with high dimensional objects and some missing values. RESULTS: Only one parameter gave significant differences at p < 0.05, GM-CSF at instillation 6. Repeated measurement analysis of variance, backward stepwise multiple logistic regression and linear discriminant analysis failed to show any significance. However, significant differences in the structure of correlation between features in the groups with and without therapeutic effect were observed and four highly informative variables (the masses of sICAM-1, TNF alpha, sCD14 and pH) relating to 5th-6th installations were selected by SWS. These features provided accurate individual prediction of therapeutic outcome for all our patients. Cross-validation analysis and computer simulation showed the statistically significant stability of the prediction. CONCLUSION: We have selected a set of urinary variables that could be considered as a perspective combination of indicators (syndromes) of outcome of pre-operation BCG therapy of patients with superficial bladder cancer. A larger patient database will provide testing and evaluation of the biological and clinical significance of selected features. The computational syndrome-disease approach should be applicable for the solution of decision-making problems for management of cancer.

Adjuvants, Immunologic↗
PubMed1995

The elements of statistics for clinical readers.

Statistical courses usually emphasize teaching the mathematical properties of methods to perform statistical significance testing. To interpret clinical studies, however, the reader needs to know only the purpose of the statistical methods, not their mathematical basis. This purpose in most studies is to evaluate the association between a risk factor and an outcome. The evaluation has five components: (1) measure the strength of the association, (2) determine the probability that the observed association did not occur by chance alone, (3) find the range of probable values for the measure of association, (4) reduce the possibility that the association is invalid because of confounding factors, and (5) examine the possibility that the association does not apply equally well to all people because of modulating factors. By focusing on the purposes of the statistical evaluation, the reader will be less distracted by the specific mathematical formulations that provide little additional information to the clinician.

Confidence Intervals↗
PubMed2006

Maximally selected chi-square statistics for ordinal variables.

The association between a binary variable Y and a variable X having an at least ordinal measurement scale might be examined by selecting a cutpoint in the range of X and then performing an association test for the obtained 2 x 2 contingency table using the chi-square statistic. The distribution of the maximally selected chi-square statistic (i.e. the maximal chi-square statistic over all possible cutpoints) under the null-hypothesis of no association between X and Y is different from the known chi-square distribution. In the last decades, this topic has been extensively studied for continuous X variables, but not for non-continuous variables of at least ordinal measurement scale (which include e.g. classical ordinal or discretized continuous variables). In this paper, we suggest an exact method to determine the finite-sample distribution of maximally selected chi-square statistics in this context. This novel approach can be seen as a method to measure the association between a binary variable and variables having an at least ordinal scale of different types (ordinal, discretized continuous, etc). As an illustration, this method is applied to a new data set describing pregnancy and birth for 811 babies.

Biometry↗
PubMed2006

Gene-dropping vs. empirical variance estimation for allele-sharing linkage statistics.

In this study, we compare the statistical properties of a number of methods for estimating P-values for allele-sharing statistics in non-parametric linkage analysis. Some of the methods are based on the normality assumption, using different variance estimation methods, and others use simulation (gene-dropping) to find empirical distributions of the test statistics. For variance estimation methods, we consider the perfect variance approximation and two empirical variance estimates. The simulation-based methods are gene-dropping with and without conditioning on the observed founder alleles. We also consider the Kong and Cox linear and exponential models and a Monte Carlo method modified from a method for finding genome-wide significance levels. We discuss the analytical properties of these various P-value estimation methods and then present simulation results comparing them. Assuming that the sample sizes are large enough to justify a normality assumption for the linkage statistic, the best P-value estimation method depends to some extent on the (unknown) genetic model and on the types of pedigrees in the sample. If the sample sizes are not large enough to justify a normality assumption, then gene-dropping is the best choice. We discuss the differences between conditional and unconditional gene-dropping.

Alleles↗
PubMed1998

Independence and statistical inference in clinical trial designs: a tutorial review.

The requirements for statistical approaches to the design, analysis, and interpretation of experimental data are now accepted by the scientific community. This is of particular importance in medical studies where public health consequences are of concern. Investigators in the clinical sciences should be cognizant of statistical principles in general, but should always be wary of the pursuing their own analyses and engage statisticians for data analysis whenever possible. Examples of circumstances that require statistical evaluation not found in textbooks and not always obvious to the lay person are pervasive. Incorrect statistical evaluation and analyses in such situations will result in erroneous and potentially serious misleading interpretation of clinical data. Although a statistician may not be responsible for any misinterpretations in such unfortunate circumstances, the quote often cited about statisticians and "damned liars" may appear to be more truth than fable. This article is a tutorial review and describes a common misuse of clinical data resulting in an apparently large sample size derived from a small number of patients. This mistake is a consequence of ignoring the dependency of results, treating multiple observations from a single patient as independent observations.

Data Interpretation, Statistical↗
PubMed2002

Medical students' perspective on the teaching of medical statistics in the undergraduate medical curriculum.

Two undergraduate medical students at the University of Bristol commented on their experiences of learning medical statistics. In general, medical students' focus is on acquiring skills needed to practice clinical medicine, and great care must be taken to explain why disciplines such as statistics and epidemiology are relevant to this. Use of real examples and an emphasis on the need for evidence has meant that medical students are increasingly aware of the pressure on clinicians to justify their treatment decisions, and the associated need to be able to understand and critically appraise medical research. It was felt that medical statistics courses should focus on critical appraisal skills rather than on the ability to analyse data, which can be acquired by particular students when they need to do this. Medical statistics should be taught early in the curriculum, but there is a need to reinforce such skills throughout the course. Teaching and assessment methods should recognize that what is being taught is a practical skill of clinical relevance. This means that problem based small groups, data interpretation exercises and objective structured clinical examinations will be more productive than traditional teaching and examination methods.

Attitude of Health Personnel↗
PubMed2007

A method for therapeutic dose selection in a phase II clinical trial using contrast statistics.

This paper proposes a statistical method for determining the therapeutic dose of a test drug in a confirmatory clinical trial based on a phase II clinical trial using 3 or 4 doses of the drug. This method assumes the primary variable has a normal distribution with a common variance, that a test-drug effect is seen when the population means show a response pattern indicating a monotonic increase with dose, and that there is a prior distribution for the population means. Under the proposed method, multiple contrast statistics are determined, such as contrast statistics for linear increase and plateau, and a response pattern is selected based on the maximum contrast statistic. The posterior probability that the selected response pattern is the true one is evaluated, and if this exceeds the cut-off value a therapeutic dose is selected based on the estimated response pattern. To select the appropriate cut-off value, a simulation study was conducted using a loss function for which the loss due to overestimation is greater than the loss due to underestimation. It was found that, as a rule, the appropriate cut-off value to reduce the expected loss for various response patterns is 0.75 for a 3-group trial and 0.70 for a 4-group trial. Using these cut-off values, the proposed method was applied to a previous clinical trial of a leukotriene receptor antagonist in patients with bronchial asthma. The method enabled the selection of what are considered appropriate response patterns and a therapeutic dose. Thus, the proposed method appears reasonable.

Asthma↗
PubMed2001

A general goodness-of-fit approach for inference procedures concerning the kappa statistic.

The kappa statistic is frequently used as a measure of agreement among two or more raters. Although considerable research on statistical inferences for this statistic has been published for the case of two raters and a binary outcome, relatively little work has appeared on inference problems for the case of multiple raters and/or polytomous nominal outcome categories. In this paper we propose a new procedure for constructing inferences for the kappa statistic that may be applied to this general case. The procedure is based on a chi-square goodness-of-fit test as applied to the Dirichlet multinomial model, and is a natural extension of previously proposed procedures that apply to more restricted cases. A simulation study shows that the new procedure provides confidence interval coverage levels and type I error rates close to nominal over a wide range of parameter combinations. We also present a sample size formula which may be used to determine the required number of subjects and raters for a given number of outcome categories.

Bias↗
PubMed1998

The statistics of synergism.

Biological scientists often want to determine whether two agents or events, for example, extracellular stimuli and/or intracellular signaling pathways, act synergistically when eliciting a biological response. When setting out to study whether two experimental treatments act synergistically, most biologists design the correct experiment--they administer four treatment combinations consisting of (1) the first treatment alone, (2) the second treatment alone, (3) both treatments together, and (4) neither treatment (i.e. the control). Many biologists are less clear about the correct statistical approach to determining whether the data collected in such an experimental design support a conclusion regarding synergism, or lack thereof. The non-additivity of two experimental treatments that is central to the definition of synergism leads to an algebraic formulation corresponding to the statistical null hypothesis appropriate for testing whether or not there is synergism. The resulting complex contrast among the four treatment group means is identical to the interaction effect tested in a two-way analysis of variance (ANOVA). This should not be surprising, because synergism, by definition, occurs when two treatments interact, rather than act independently, to influence a biological response. Hence, in the most readily implemented approach, the correct statistical analysis of a question of synergism is based on testing the interaction effect in a two-way ANOVA. This review presents the rationale for this correct approach to analysing data when the question is of synergism, and applies this approach to a recent published example. In addition, a common incorrect approach to analysing data with regards to synergism is presented. Finally, several associated statistical issues with regard to correctly implementing a two-way ANOVA are discussed.

Analysis of Variance↗