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At least 19 recordsLinked to original sources
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↗
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↗
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↗
PubMed1989

Statistical significance versus clinical relevance. Part I. The essential role of the power of a statistical test.

When comparing two treatment groups, hypothesis testing is widely used. However, clinical trialists should be more interested in statistical methods which elicit the magnitude of the differences between treatment groups, rather than a simple indication of whether or not the differences are statistically significant. Statistical significance does not necessarily imply clinical relevance. If the true difference between two treatment groups is so small that it is clinically irrelevant, a sample size can be found for which this difference is statistically significant. On the other hand, if the difference between treatment groups is statistically non-significant, it may still be clinically important. The limitations of conventional hypothesis testing of equal true means as such are highlighted. The need to control the power of the test--which takes into account the difference in treatment means which is considered important (clinically relevant) by the researcher--is discussed.

Clinical Trials as Topic↗
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↗
PubMed1998

Recommendations for statistical designs of in vivo mutagenicity tests with regard to subsequent statistical analysis.

A workshop was held on September 13 and 14, 1993, at the GSF, Neuherberg, Germany, to start a discussion of experimental design and statistical analysis issues for three in vivo mutagenicity test systems, the micronucleus test in mouse bone marrow/peripheral blood, the chromosomal aberration tests in mouse bone marrow/differentiating spermatogonia, and the mouse dominant lethal test. The discussion has now come to conclusions which we would like to make generally known. Rather than dwell upon specific statistical tests which could be used for data analysis, serious consideration was given to test design. However, the test design, its power of detecting a given increase of adverse effects and the test statistics are interrelated. Detailed analyses of historical negative control data led to important recommendations for each test system. Concerning the statistical sensitivity parameters, a type I error of 0.05 (one tailed), a type II error of 0.20 and a dose related increase of twice the background (negative control) frequencies were generally adopted. It was recommended that sufficient observations (cells, implants) be planned for each analysis unit (animal) so that at least one adverse outcome (micronucleus, aberrant cell, dead implant) would likely be observed. The treated animal was the smallest unit of analysis allowed. On the basis of these general consideration the sample size was determined for each of the three assays. A minimum of 2000 immature erythrocytes/animal should be scored for micronuclei from each of at least 4 animals in each comparison group in the micronucleus assays. A minimum of 200 cells should be scored for chromosomal aberrations from each of at least 5 animals in each comparison group in the aberration assays. In the dominant lethal test, a minimum of 400 implants (40-50 pregnant females) are required per dose group for each mating period. The analysis unit for the dominant lethal test would be the treated male unless the background frequency of dead implants (DI) is so low that multiple males would need to be integrated to meet the minimum observation of one adverse outcome (DI) per analysis unit. A three-step strategy of data analysis was proposed for the cytogenetic assays. Use of negative historical controls was allowed in certain circumstances for interpretation of results from micronucleus tests and chromosomal aberration tests.

Animals↗
PubMedDate not supplied

Looking for statistical stability: a new method of evaluating reliability of statistical tests.

A new method of looking for statistical reliability, stability calculation, is described and is applied to statistical tests. Alike the power of statistical tests, stability calculation enables us to assess reliability of the latter. It belongs to the category of subsampling techniques that require using subsamples taken from the original sample. It provides descriptive and non-inferential results indicating the stability percentage: the percentage of sample elements to be removed, in order to change results obtained with the original sample. The higher is the stability percentage the more reliable is the statistical test. Stability percentage and power are correlated. Stability calculation provides informations about the elements in the sample, the most powerful points.

Computer Simulation↗
PubMed1998

Statistical quality control methods in infection control and hospital epidemiology, Part II: Chart use, statistical properties, and research issues.

This is the second in a two-part series discussing and illustrating the application of statistical process control (SPC) in hospital epidemiology. The basic philosophical and theoretical foundations of statistical quality control and their relation to epidemiology are emphasized in order to expand the mutual understanding and cross-fertilization between these two disciplines. Part I provided an overview of the philosophy and general approach of SPC, illustrated common types of control charts, and provided references for further information or statistical formulae. Part II now discusses alternate possible SPC approaches, statistical properties of control charts, chart-design issues and optimal control limit widths, some common misunderstandings, and more advanced issues. The focus of both articles is mostly nonmathematical, emphasizing important concepts and practical examples rather than academic theory and exhaustive calculations.

Data Display↗
PubMed1989

Survey of cause-of-death query criteria used by state vital statistics programs in the US and the efficacy of the criteria used by the Oregon Vital Statistics Program.

A survey of the 52 vital statistics registration areas in the United States revealed that at least 23 did not fulfill the minimum cause-of-death query guidelines recommended by the National Center for Health Statistics. The Oregon Center for Health Statistics is one of only a few that query certifying physicians at a comprehensive level. During August 1986-July 1987, a total of 2,453 of 23,238 death certificates were returned to the certifiers for additional information, not including those returned in a tobacco use study. More than one-half (56.1 per cent) resulted in new and more specific underlying cause-of-death data. Only 5.2 per cent of the queries were unanswered. One probable result of Oregon's program is that the state has the highest percentage of liver cirrhosis and disease deaths attributed to alcohol abuse in the United States. Nationally, 41.7 per cent of all liver disease and cirrhosis deaths in 1984 were listed as due to alcohol compared to 82.4 per cent in Oregon. The state's total liver cirrhosis and disease death rate (12.0 per 100,000 population) is only marginally higher than the United States rate (11.6). The query program also serves to locate maternal deaths that would otherwise not be reported, as well as to provide more accurate cause-of-death statistics in general.

Cause of Death↗
PubMedDate not supplied

Statistical tests (Part 1): Descriptive statistics.

This series of three articles has been designed to facilitate an understanding of some commonly used statistical terms encountered when reading research articles. In addition, it is hoped that the nurse researcher who has access to a personal computer containing basic statistical software will gain some insight into which statistical tests to use and in what circumstances. The articles do not attempt to provide any understanding of the mathematics of the statistical tests employed.

Humans↗