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The NISAN system is a new interactive statistical analysis program package constructed by an organization of Japanese statisticans. The package is widely available for both statistical situations, confirmatory analysis and exploratory analysis, and is planned to obtain statistical wisdom and to choose optimal process of statistical analysis for senior statisticians.
This article draws together statistical information in several broad areas that relate to women's health, women's reproductive activities and women's occupations in Sweden. The statistical analysis reflects the major changes that have occurred in Swedish society and that have had a major impact on the health and well-being, as well as on the social participation rate, of women. Much of the data is drawn from a recent special effort at Statistic Sweden aimed at influencing the classification, collection and presentation of statistical data in all fields in such a way that family, working, education, health and other conditions of women can be more readily and equitably compared with those of men. In addition, social changes have seen the shifting of the responsibility of health care from the unpaid duties of women in the home to health care institutions, where female employees predominate. These trends are also discussed.
Surveys have been, and will most likely continue to be, the source of data for many empirical articles. Likewise, the difficulty of making valid statistical inferences in the face of missing data will continue to plague researchers. In an ideal situation, all potential survey participants would respond; in reality, the goal of an 80 to 90% response rate is very difficult to achieve. When nonresponse is systematic, the combination of low response rate and systematic differences can severely bias inferences that are made by the researcher to the population. It is important for the researcher to assess the potential causes of nonresponse and the differences between the observed values in the sample compared to what may have been gained if the sample was complete, particularly when the response rate is low. There are methods available that substitute imputed values for missing data, but these methods are useless if the researcher lacks knowledge of how the responders and nonresponders may differ. With regard to statistical inference, the researcher also should be aware of the difference between a convenient sample and a probability sample. Valid statistical inference assumes that the probability of characteristics observed in the sample bear some relationship to their occurrence in the population. For example, in a simple random sample each member of the accessible population has an equal chance of inclusion in the sample. A convenient sample lacks the statistical properties of a probability sample that allow the validity of its inferences to be assessed strictly from a mathematical framework. The context of the research and the type of data being gathered greatly affect the validity of any generalizations the researcher makes with regard to the population the convenient sample attempts to represent.
The Internet is expanding the options for locating health statistics. Electronic versions of print resources and access to searchable databases provide new avenues to statistical data. This article identifies and describes major Internet sites which provide free public access to health statistics. Covered are sites for United States federal and state data, international statistics, as well as subject specific sites for AIDS, aging, and substance abuse.
OBJECTIVE: To discuss the use of statistical graphics in the analysis of pharmacokinetics and pharmacodynamics data. METHODS: Information on graphic techniques and their application was retrieved from a MEDLINE search (January 1980-March 1997) of the English-language literature and bibliographic reviews of review articles and books. Data used to generate plots were extracted from some new drug applications submitted to the Food and Drug Administration and by simulation. DATA SYNTHESIS: In carrying out data analysis, we should look at data in several ways, construct a number of plots, and do several analyses, letting the results of each step suggest the next. The information from a plot should be relevant to the goals of the analysis. Thus, in choosing a graphic method, it is necessary to match the capabilities of the method to the need in the context of the application. For example, if linear relationships among variables in a set of multidimensional data are relevant, scatter plots such as the pairs plot with smoothing is likely to be more informative than other graphic methods. It is necessary to recognize what kinds of perceived structure are attributable to the data, and what kinds are artifacts of the display technique itself when using graphs for data analysis. CONCLUSIONS: Graphic techniques enable the data analyst to explore data thoroughly, look for patterns and relationships, confirm or disprove the expected, and discover new phenomena. An important element of statistical graphic techniques is flexibility, both in tailoring the analysis to the structure of the data and in responding to patterns that successive steps of analysis uncover. Statistical graphics can and should be used to enhance numeric statistical analyses.
AIM: This paper highlights some of the areas where there are problems with the way that statistics are conducted and reported in psychology journals. Recommendations are given for improving these problems. SAMPLE: The choice of topics is based largely on the questions that authors, reviewers, and editors have asked in recent years. The focus is on null hypothesis significance testing (NHST), choosing a statistical test, and what should be included in results sections. RESULTS: There are several ways to improve how statistics are reported. These should improve both the authors' and the readers' understanding of the data. CONCLUSIONS: Psychology as a discipline will improve if the way in which statistics are conducted and reported is improved. This will require effort from authors, scrutiny from reviewers, and stubbornness from editors.
Several lines of evidence suggest that the image statistics of the environment shape visual abilities. To date, the image statistics of natural scenes and faces have been well characterized using Fourier analysis. We employed Fourier analysis to characterize images of signs in American Sign Language (ASL). These images are highly relevant to signers who rely on ASL for communication, and thus the image statistics of ASL might influence signers' visual abilities. Fourier analysis was conducted on 105 static images of signs, and these images were compared with analyses of 100 natural scene images and 100 face images. We obtained two metrics from our Fourier analysis: mean amplitude and entropy of the amplitude across the image set (which is a measure from information theory) as a function of spatial frequency and orientation. The results of our analyses revealed interesting differences in image statistics across the three different image sets, setting up the possibility that ASL experience may alter visual perception in predictable ways. In addition, for all image sets, the mean amplitude results were markedly different from the entropy results, which raises the interesting question of which aspect of an image set (mean amplitude or entropy of the amplitude) is better able to account for known visual abilities.
BACKGROUND: The author discusses the shortcomings of using statistical significance testing as a method to infer that results of periodontal clinical trials are clinically meaningful. To compensate for these deficiencies, he also identifies criteria and periodontal parameters that can be used to reflect clinically significant outcomes. TYPES OF STUDIES REVIEWED: The author searched the medical and dental literature to identify commentaries that addressed the problems associated with interpreting statistical significance testing, or hypothesis testing, and defining clinical significance. RESULTS: The limitations of statistical significance testing related to identifying clinically significant changes include failure to indicate if the detected differences between variables in test and control groups are large or important. After reviewing various definitions of the term "clinical significance," the author reviews and proposes a comprehensive working definition of it. Regarding the efficacy of periodontal therapy, he delineates the advantages and limitations of specific criteria (such as absolute values, cut points) that can be used to define clinical significance. CLINICAL IMPLICATIONS: The author suggests that clinically significant results should be defined before initiating a study and statistical significance testing should be used to validate that findings did not occur by chance. This would help place the importance of clinical data into perspective, and it would enhance clinicians' ability to select the most appropriate therapies for particular sites in periodontal patients.
The statistical analysis of non-transformed and logarithm-transformed blood- to breath-alcohol ratios ("blood/breath ratios") is detailed. The data analyzed were derived from 137 simultaneous blood-alcohol and breath-alcohol concentration measurements made between 15 and 179 min after the end of drinking, with 136 of the measurements obtained during the 15- to 124-min time frame. Although the distribution of the non-transformed ratios is positively skewed, and that of the logarithm-transformed data more closely approximates the normal distribution upon visual inspection, both analyses generated results that do not differ significantly from each other when considered in the context of "mean ratios + or - 2SD". This is in accord with the results of the Kolmogorov-Smirnov goodness-of-fit test, which does not reject either dataset and demonstrates that both are approximately normal. Since the logarithm-transformed data generate more conservative statistical blood/breath ratio ranges than the non-transformed data, they were selected as the basis for the principal conclusion of this work. That conclusion is a refutation of the argument that, breath-alcohol analyzers relying on a 2100:1 blood/breath ratio tend to underestimate the blood-alcohol concentrations of driving-while-intoxicated arrestees because the commonly accepted mean postabsorptive ratio is 2300:1. In fact, whenever the absorption status of a driving-while-intoxicated arrestee at the time of a breath test cannot be definitively established, the results of this work support the application of a relative error range of -40% to +28% for 95% of the population, based on a statistical blood/breath ratio range of 1259:1 to 2679:1, and -46% to +42% for 99% of the population, based on a statistical range of 1128:1 to 2989:1.
The effect of seasons on suicides has been suggested repeatedly. In order to reveal a true seasonal pattern, an appropriate statistical technique, which is sensitive to a specific type of cyclic variation in the data, must be chosen. This study is a review of the use of statistical techniques for seasonality and of some important characteristics of study samples that were evaluated from 46 original suicide seasonality articles published in major psychiatric journals. The results showed that statistical techniques were applied in a majority of articles, but they were commonly lacking regarding analyses, which compared seasonal patterns among subgroups of a population. In recent studies more sophisticated statistical techniques were utilized for seasonality, like spectral analyses, as compared with earlier studies, in which the emphasis was on chi-square tests. Lack of reporting essential features of the data, such as the sample size and monthly values of suicides, were frequent. The calendar effect was adjusted only in 11 studies. Some recommendations concerning the methodological and reporting issues are summarized for future articles on the seasonal affect on suicides.
An accurate statistical energy function that is suitable for the prediction of protein structures of all classes should be independent of the structural database used for energy extraction. Here, two high-resolution, low-sequence-identity structural databases of 333 alpha-proteins and 271 beta-proteins were built for examining the database dependence of three all-atom statistical energy functions. They are RAPDF (residue-specific all-atom conditional probability discriminatory function), atomic KBP (atomic knowledge-based potential), and DFIRE (statistical potential based on distance-scaled finite ideal-gas reference state). These energy functions differ in the reference states used for energy derivation. The energy functions extracted from the different structural databases are used to select native structures from multiple decoys of 64 alpha-proteins and 28 beta-proteins. The performance in native structure selections indicates that the DFIRE-based energy function is mostly independent of the structural database whereas RAPDF and KBP have a significant dependence. The construction of two additional structural databases of alpha/beta and alpha + beta-proteins further confirmed the weak dependence of DFIRE on the structural databases of various structural classes. The possible source for the difference between the three all-atom statistical energy functions is that the physical reference state of ideal gas used in the DFIRE-based energy function is least dependent on the structural database.
We consider the Wright-Fisher model with exponential population growth and investigate effects of population growth on the shape of genealogy and the distributions of several test statistics of neutrality. In the limiting case as the population grows rapidly, the rapid-growth-limit genealogy is characterized. We obtained approximate expressions for expectations and variances of test statistics in the rapid-growth-limit genealogy and star genealogy. The distributions in the star genealogy are narrower than those in the cases of the simulated and rapid-growth-limit genealogies. The expectations and variances of the test statistics are monotone decreasing functions of the time length of the expansion, and the higher power of R(2) against population growth is suggested to be due to their smaller variances rather than to change of the expectations. We also investigated by simulation how quickly the distributions of test statistics approach those of the rapid-growth-limit genealogy.
Several analytic techniques have been used to determine sexual dimorphism in vertebrate morphological measurement data with no emergent consensus on which technique is superior. A further confounding problem for frog data is the existence of considerable measurement error. To determine dimorphism, we examine a single hypothesis (Ho = equal means) for two groups (females and males). We demonstrate that frog measurement data meet assumptions for clearly defined statistical hypothesis testing with statistical linear models rather than those of exploratory multivariate techniques such as principal components, correlation or correspondence analysis. In order to distinguish biological from statistical significance of hypotheses, we propose a new protocol that incorporates measurement error and effect size. Measurement error is evaluated with a novel measurement error index. Effect size, widely used in the behavioral sciences and in meta-analysis studies in biology, proves to be the most useful single metric to evaluate whether statistically significant results are biologically meaningful. Definitions for a range of small, medium, and large effect sizes specifically for frog measurement data are provided. Examples with measurement data for species of the frog genus Leptodactylus are presented. The new protocol is recommended not only to evaluate sexual dimorphism for frog data but for any animal measurement data for which the measurement error index and observed or a priori effect sizes can be calculated.
OBJECTIVE: To describe the use of conventional tests of statistical significance and the current trends shown by their use in three biomedical journals read in Spanish-speaking countries. METHODS: All descriptive or explanatory original articles published in the five-year period of 1996 through 2000 were reviewed in three journals: Revista Cubana de Medicina General Integral [Cuban Journal of Comprehensive General Medicine], Revista Panamericana de Salud Pública/Pan American Journal of Public Health, and Medicina Clínica [Clinical Medicine] (which is published in Spain). RESULTS: In the three journals that were reviewed various shortcomings were found in their use of hypothesis tests based on P values and in the limited use of new tools that have been suggested for use in their place: confidence intervals (CIs) and Bayesian inference. The basic findings of our research were: minimal use of CIs, as either a complement to significance tests or as the only statistical tool; mentions of a small sample size as a possible explanation for the lack of statistical significance; a predominant use of rigid alpha values; a lack of uniformity in the presentation of results; and improper reference in the research conclusions to the results of hypothesis tests. CONCLUSIONS: Our results indicate the lack of compliance by authors and editors with accepted standards for the use of tests of statistical significance. The findings also highlight that the stagnant use of these tests continues to be a common practice in the scientific literature.
An appropriate statistical methodology in toxicity studies has been discussed over the last two decades and many statistical methods have already been proposed. Many practical problems, however, still remain unresolved and most pharmaceutical industries have been using a tree-type algorithm routinely to analyze repeated-dose toxicity study data. In considering routine use of statistical analysis in toxicological studies, standardization of statistical methodology is necessary and the decision tree has an important role. In this article, the problems, relating to tree-type algorithms are summarized. Then we propose a new tree-type algorithm, which targets quantitative data in repeated-dose studies in rodents, usually sample size per group between 10 to 20, based on the following two important principles: "using a parametric method" and "suitable for intuition of toxicologists". An example of its application to actual toxicity study data is demonstrated. The performance of this new method is also evaluated using historical data. However, it should be noted that the intention of this paper is not to make a definite solution of the decision tree. Several other alternatives can be considered. Since there is no single theoretically correct solution of tree-type algorithms, too formal a use of the decision tree is not recommended. We must not forget the exploratory nature of evaluating repeated toxicity data.
The increasing number of economic evaluations of healthcare interventions, and of drug therapies in particular, has been well documented. However, surveys have demonstrated that standards of conduct of such studies have not similarly increased. Of particular concern is the lack of development or even consideration of statistical techniques in the reporting of studies. This article addresses issues that must be considered both in the conduct and in the assessment of the quality of studies. Throughout the paper, examples of pharmacoeconomic analyses are used to illustrate the points made. Recommendations for the conduct of future pharmacoeconomic studies are given. Such recommendations specifically relate to the level of testing that is conducted, the choice of statistical tests and the manner in which statistical significance is reported. In addition, existing methods for the statistical analysis of cost-effectiveness ratios and for the determination of sample size in economic evaluations are discussed, and a partial solution to this issue is offered.
Clinical articles published in Japanese journals are said to be characterized by poor study design, less sophisticated statistics, and producing few high-grade clinical evidences. Two American and two Japanese medical journals, published in 1990, 1993, 1996, and 1999 were compared to find out the differences regarding study design, statistical methods, and level of clinical evidence of original articles and synthetic studies. There were 1689 original articles in American and 308 in Japanese joumals. Regarding study design, American articles contributed much more to randomized controlled trials/controlled trials/clinical trials (27.9% vs. 14.3%, p=0.001), cohort studies (21.6% vs. 6.2%, p=0.001), and case-control studies (6.5% vs.0.3 %, p=0.000). Among original articles in American and Japanese journals, mean number of statistical methods used were 2.4 and 1.7 per article (p=0.000), respectively. Articles providing high grade clinical evidence (grade Ia, Ib & IIa) were much greater in proportion in American journals than Japanese journals (31.1% vs. 12.7%, p=0.001). The overall picture of Japanese medical articles seems to be improving recently, at least in terms of statistical methods toward more diversified and sophisticated way of use, compared to the previous data.
The objective of this study was to examine the relationship between monthly Dairy Herd Improvement (DHI) subclinical mastitis and new infection rate estimates and daily bulk tank somatic cell count (SCC) summarized by statistical process control tools. Dairy Herd Improvement Association test-day subclinical mastitis and new infection rate estimates along with daily or every other day bulk tank SCC data were collected for 12 mo of 2003 from 275 Upper Midwest dairy herds. Herds were divided into 5 herd production categories. A linear score [LNS = ln(BTSCC/100,000)/0.693147 + 3] was calculated for each individual bulk tank SCC. For both the raw SCC and the transformed data, the mean and sigma were calculated using the statistical quality control individual measurement and moving range chart procedure of Statistical Analysis System. One hundred eighty-three herds of the 275 herds from the study data set were then randomly selected and the raw (method 1) and transformed (method 2) bulk tank SCC mean and sigma were used to develop models for predicting subclinical mastitis and new infection rate estimates. Herd production category was also included in all models as 5 dummy variables. Models were validated by calculating estimates of subclinical mastitis and new infection rates for the remaining 92 herds and plotting them against observed values of each of the dependents. Only herd production category and bulk tank SCC mean were significant and remained in the final models. High R2 values (0.83 and 0.81 for methods 1 and 2, respectively) indicated a strong correlation between the bulk tank SCC and herd's subclinical mastitis prevalence. The standard errors of the estimate were 4.02 and 4.28% for methods 1 and 2, respectively, and decreased with increasing herd production. As a case study, Shewhart Individual Measurement Charts were plotted from the bulk tank SCC to identify shifts in mastitis incidence. Four of 5 charts examined signaled a change in bulk tank SCC before the DHI test day identified the change in subclinical mastitis prevalence. It can be concluded that applying statistical process control tools to daily bulk tank SCC can be used to estimate subclinical mastitis prevalence in the herd and observe for change in the subclinical mastitis status. Single DHI test day estimates of new infection rate were insufficient to accurately describe its dynamics.