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At least 19 recordsLinked to original sources
PubMed1954

TYPES of vital statistics available in different countries: Demographic and Social Statistics Branch, Statistical Office of the United Nations.

This article analyses the types of vital statistical data available in the 58 major statistical areas of the world, covering about half the world population. The information for the rest of the world can be assumed to be even less complete.Tables are given showing the number of areas supplying information for the various tabulations in the vital statistics section of the United Nations Demographic Yearbook, and the availability of each tabulation for each area. Other tables indicate which items of substantive interest are given on national statistical-report forms in the same areas, and with what frequency.

Birth Rate↗
PubMed2001

Appointment of statistical editor and quality of statistics in a small medical journal.

AIM: To test if the appointment of a statistical editor improves the quality of manuscripts published in a small general medical journal. METHODS: Retrospective review of all manuscripts containing statistical data published in the Croatian Medical Journal between 1992 and 2000 (n=241). Statistical analysis and its presentation were assessed by a single observer. RESULTS: Before the appointment of statistical editor in 1996, 97 manuscripts with statistical data were published. Statistics was not satisfactory in 52 (54%) of them, including 26 definite errors in analysis and 43 in presentation. After the appointment of statistical editor, 144 manuscripts containing statistical data were published. Statistics was not satisfactory in 91 (63%) of them, with 51 definite errors in analysis and 69 in presentation. Out of 144 manuscripts, the editor-in-chief sent out 30 (21%) for statistical review. Statistics was not satisfactory in 25 of them, including 11 definite errors in analysis and 17 in presentation. Statistical editors comments improved three manuscripts. If the authors had acknowledged all statistical editors suggestions, 9 more manuscripts would have been improved. Statistical editor had a total of 195 comments on 30 published manuscripts. Most numerous were the comments concerning the presentation of the statistical analysis (51%), followed by the general comments (26%), comments on analysis (11%), study design (8%), and interpretation (4%). CONCLUSION: Appointment of a statistical editor is not a guarantee of improvement of statistics in small journals. Other measures are necessary, including strict editorial policy on statistical review, monitoring of revised manuscript versions, and enrollment of formally trained biostatisticians.

Humans↗
PubMed1986

[The meaning of statistical data in medical science and their examination--true and false analysis of statistical data].

The subjects which are often encountered in the statistical design and analysis of data in medical science studies were discussed. The five topics examined were: Medical science and statistical methods So-called mathematical statistics and medical science Fundamentals of cross-tabulation analysis of statistical data and inference Exploratory study by multidimensional data analyses Optimal process control of individual, medical science and informatics of statistical data In I, the author's statistico-mathematical idea is characterized as the analysis of phenomena by statistical data. This is closely related to the logic, methodology and philosophy of science. This statistical concept and method are based on operational and pragmatic ideas. Self-examination of mathematical statistics is particularly focused in II and III. In II, the effectiveness of experimental design and statistical testing is thoroughly examined with regard to the study of medical science, and the limitation of its application is discussed. In III the apparent paradox of analysis of cross-tabulation of statistical data and statistical inference is shown. This is due to the operation of a simple two- or three-fold cross-tabulation analysis of (more than two or three) multidimensional data, apart from the sophisticated statistical test theory of association. In IV, the necessity of informatics of multidimensional data analysis in medical science is stressed. In V, the following point is discussed. The essential point of clinical trials is that they are not based on any simple statistical test in a traditional experimental design but on the optimal process control of individuals in the information space of the body and mind, which is based on a knowledge of medical science and the informatics of multidimensional statistical data analysis.

Statistics as Topic↗
PubMed1997

[Statistical analysis of pharmacological data: use of cumulative chi-squared statistic].

The cumulative chi-squared statistic has been proposed for testing against ordered alternatives in various statistical models. As usual statistical tests of ordered column categorical data, the chi 2 test, Fisher's exact test and Wilcoxon test are used. Pharmacological studies often are performed by multiple dosing. Data obtained from these studies are called ordered categorical data. The cumulative chi-squared statistic, which has been proposed by Hirotsu and Shibuya for testing against ordered alternatives in various statistical models, is little used in spite of its good applicability in the field of pharmacology. This method was too difficult for the general pharmacologist and biological scientists because it requires the use of a complex matrix and a powerful computer to carry out the analysis. However since a more simple method was proposed by Matsumoto and Yoshimura this method has been used more frequently in the biological sciences. In this paper, the one way cumulative chi-squared statistic test and two way chi-squared statistic test are compared with the chi-squared statistic test and Wilcoxon test.

Data Interpretation, Statistical↗
PubMed1999

Statistical limitations in functional neuroimaging. II. Signal detection and statistical inference.

The field of functional neuroimaging (FNI) methodology has developed into a mature but evolving area of knowledge and its applications have been extensive. A general problem in the analysis of FNI data is finding a signal embedded in noise. This is sometimes called signal detection. Signal detection theory focuses in general on issues relating to the optimization of conditions for separating the signal from noise. When methods from probability theory and mathematical statistics are directly applied in this procedure it is also called statistical inference. In this paper we briefly discuss some aspects of signal detection theory relevant to FNI and, in addition, some common approaches to statistical inference used in FNI. Low-pass filtering in relation to functional-anatomical variability and some effects of filtering on signal detection of interest to FNI are discussed. Also, some general aspects of hypothesis testing and statistical inference are discussed. This includes the need for characterizing the signal in data when the null hypothesis is rejected, the problem of multiple comparisons that is central to FNI data analysis, omnibus tests and some issues related to statistical power in the context of FNI. In turn, random field, scale space, non-parametric and Monte Carlo approaches are reviewed, representing the most common approaches to statistical inference used in FNI. Complementary to these issues an overview and discussion of non-inferential descriptive methods, common statistical models and the problem of model selection is given in a companion paper. In general, model selection is an important prelude to subsequent statistical inference. The emphasis in both papers is on the assumptions and inherent limitations of the methods presented. Most of the methods described here generally serve their purposes well when the inherent assumptions and limitations are taken into account. Significant differences in results between different methods are most apparent in extreme parameter ranges, for example at low effective degrees of freedom or at small spatial autocorrelation. In such situations or in situations when assumptions and approximations are seriously violated it is of central importance to choose the most suitable method in order to obtain valid results.

Biometry↗
PubMed2002

The power and statistical behaviour of allele-sharing statistics when applied to models with two disease loci.

We have evaluated the power for detecting a common trait determined by two loci, using seven statistics, of which five are implemented in the computer program SimWalk2, and two are implemented in GENEHUNTER. Unlike most previous reports which involve evaluations of the power of allele-sharing statistics for a single disease locus, we have used a simulated data set of general pedigrees in which a two-locus disease is segregating and evaluated several nonparametric linkage statistics implemented in the two programs. We found that the power for detecting linkage using the S(all) statistic in GENEHUNTER (GH, version 2.1), implemented as statistic E in SimWalk2 (version 2.82), is different in the two. The P values associated with statistic E output by SimWalk2 are consistently more conservative than those from GENEHUNTER except when the underlying model includes heterogeneity at a level of 50% where the P values output are very comparable. On the other hand, when the thresholds are determined empirically under the null hypothesis, S(all) in GENEHUNTER and statistic E have similar power.

Alleles↗
PubMed1997

Spatial statistical analysis of Chinese cancer mortality: a comparison study of the D statistic.

In this paper, we study a nonparametric spatial pattern test statistic, the D statistic. The D statistic is an effective test statistic for testing spatial patterns in regional health data. Comparison studies of the D statistic with fixed weights and random weights are illustrated on the atlas of the Chinese cancer mortality rates and on other cancer atlas. Some social, economic and environmental reasons for statistically significant spatial autocorrelations of the Chinese cancer mortality rates were given in the discussion section. The method for calculating the mean and the variance of the randomly weighted D statistic is given in the appendix.

China↗
PubMed1983

A comparison between the sciences of epidemiology and statistics based on an examination of epidemiological or statistical studies on diabetes in Japan.

The authors selected 24 original papers which were regarded them as the epidemiological study and the statistical study from their titles, from the end of World War II to 1981. And these papers were selected from 3 medical journals of internal medicine, other medical journals and proceedings of 2 International Conferences (see Table 1), and also were the object of study, namely, theoretical considerations. Besides we classified these 24 papers into 2 sorts; papers for an epidemiological study and a statistical study, and made a comparative study of details of these papers theoretically. As the result we were able to clarify what the authors of 24 papers had considered about the natures of epidemiology and statistics as the science. It was clarified that two sciences, epidemiology and statistics, had been in the general trend without any recognition of the differences between two. And as the conclusion we pointed out that the field of activity of statistics was broader than that of epidemiology, and the nature of statistics as the science might be changeable according to the object, moreover, statistical theory might be a branch of mathematics and so on.

Diabetes Mellitus↗
PubMed1994

Issues in biomedical statistics: statistical inference.

The first step in making inferences under the frequentist system of statistical logic is to propose a null hypothesis. An experiment is then performed, or a set of observations made. The resulting data are subjected to statistical analysis to determine whether the null hypothesis should be rejected or not. If it is, then some alternative hypothesis must have been entertained. In biomedical work, the alternative hypothesis should usually be non-specific and it follows that the statistical test of the null hypothesis should be interpreted in a two-sided fashion. The decision to reject or accept statistical null hypotheses, whether on the basis of a P value or confidence intervals, is probabilistic in nature and always attended by the risk of error. It is argued that, in biomedical research, it is the risk of making false-positive statistical inferences (Type I error) that should be most closely controlled. The risks of Type I error cannot be considered in isolation from the model of inference under which the null hypothesis is tested. That which forms the basis for using the classical t, F and X2 tests is the population model, in which the inference is referred to a defined population that has been randomly sampled and which conforms to a specified frequency distribution. Under this model, serious errors in statistical inference can occur if the actual distributions of the populations do not conform to those specified by theory. More importantly, the population model is inappropriate to most biomedical research, in which treatment groups are created by randomization but not by random sampling.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗
PubMed2003

Adjusting for population heterogeneity: a framework for characterizing statistical information and developing efficient test statistics.

The focus of this work is the TDT-type and family-based test statistics used for adjusting for potential confounding due to population heterogeneity or misspecified allele frequencies. A variety of heuristics have been used to motivate and derive these statistics, and the statistics have been developed for a variety of analytic goals. There appears to be no general theoretical framework, however, that may be used to evaluate competing approaches. Furthermore, there is no framework to guide the development of efficient TDT-type and family-based methods for analytic goals for which methods have not yet been proposed. The purpose of this paper is to present a theoretical framework that serves both to identify the information which is available to methods that are immune to confounding due to population heterogeneity or misspecified allele frequencies, and to inform the construction of efficient unbiased tests in novel settings. The development relies on the existence of a characterization of the null hypothesis in terms of a completely specified conditional distribution of transmitted genotypes. An important observation is that, with such a characterization, when the conditioning event is unobserved or incomplete, there is statistical information that cannot be exploited by any exact conditional test. The main technical result of this work is an approach to computing test statistics for local alternatives that exploit all of the available statistical information.

Data Interpretation, Statistical↗
PubMed2003

Further statistics in dentistry. Part 9: Bayesian statistics.

Statistics can be defined as the methods used to assimilate data, so that guidance can be given, and conclusions drawn, in situations which involve uncertainty. In particular, statistical inference is concerned with drawing conclusions about particular aspects of a population when that population cannot be studied in full. Uncertainty arises here because the totality of the information is not available. Instead, to make inferences about the population, it is necessary to rely on a sample of data which is selected from the population; this sample data may be augmented, in certain circumstances, by auxiliary information which is obtained independently of the sample data. Clearly, uncertainty lies at the heart of statistics and statistical inference. This uncertainty is measured by a probability which therefore forms the crux of statistics and must be properly understood in order to interpret a statistical analysis.

Algorithms↗
PubMed2003

Tutorials in clinical research: part VII. Understanding comparative statistics (contrast)--part A: general concepts of statistical significance.

OBJECTIVES/HYPOTHESIS: The present tutorial is the seventh in a series of Tutorials in Clinical Research. The specific purpose of the tutorial (Part A) and its sequel (Part B) is to introduce and explain three commonly used statistical tools for assessing contrast in the comparison between two groups. STUDY DESIGN: Tutorial. METHODS: The authors met weekly for 10 months discussing clinical research studies and the applied statistics. The difficulty was not in the material but in the effort to make the report easy to read and as short as possible. RESULTS: The tutorial is organized into two parts. Part A, which is the present report, focuses on the fundamental concepts of the null hypothesis and comparative statistical significance. The sequel, Part B, discusses the application of three common statistical indexes of contrast, the chi2, Mann-Whitney U, and Student t tests. CONCLUSIONS: Assessing the validity of medical studies requires a working knowledge of research design and statistics; obtaining this knowledge need not be beyond the ability of the busy surgeon. The authors have tried to construct an accurate, easy-to-read, easy-to-apply, basic introduction to comparing two groups. The long-term goal of the present tutorial and others in the series is to facilitate basic understanding of clinical research, thereby stimulating reading of some of the numerous well-written research design and statistical texts. This knowledge may then be applied to the continuing educational review of the literature and the systematic prospective analysis of individual practices.

Bias↗
PubMed1998

Statistical methods in epidemiology: I. Statistical errors in hypothesis testing.

PURPOSE: Although scientific journal editors are making use of statisticians in the review process, the quality of statistical reporting in many journals remains poor. In many cases the problem for the scientist would appear to be a lack of understanding of basic statistics. The focus of the scientist is on showing 'p < 0.05', when what is actually required is a statement about effect size and interval estimation. The aim of this paper is to show the inadequacy of reporting of results using p-values alone. This paper is the first in a series detailing common statistical methods, with a view to aiding potential authors in their statistical presentation of data. METHOD: A review of the basic hypothesis test, using examples from the author's own teaching experiences. RESULTS: Type I and type II errors are defined; the problem of multiple comparisons is highlighted; interval estimation is introduced. CONCLUSIONS: The case for considering the p-value as an error probability is made which suggests ways of improving statistical presentation and thus expediting the statistical review process.

Confidence Intervals↗