Statistical Analysis Software Package Research Articles

Data Preparation 101: How To Use Query-By-Example To Get Your Research Dataset Ready For Primetime

Researchers are often distressed to discover that the data they wanted to use in their landmark study is not configured in a way that is usable by a Statistical Analysis Software Package (SASP). For example, the data needed may come from two or more sources and it may not be clear to the researcher how to get them combined into one analyzable dataset. Fortunately, there is hope. The query facility within database management software (DBMS), such as Microsoft Access, is particularly well-equipped to do the data reconfigurations necessary to get datasets ready for a SASP. Unfortunately, most researchers have either never been exposed to a DBMS or are unaware of the powerful data-transforming queries that they can perform. The purpose of this paper is to introduce researchers to some very helpful and relatively easy to learn techniques for solving common dataset misconfiguration problems. It is presented in three sets of hands-on examples: (1) how to import a dataset from an electronic spreadsheet into a database table, (2) how to use an easy-to-learn DBMS facility called Query-By-Example (QBE) to perform specific data reconfiguration tasks, and (3) how to import the reconfigured dataset from the DBMS into a SASP. The software used is MS Excel, MS Access, and SPSS (originally called Statistical Package for the Social Sciences). The examples are presented step-by-step so that the reader can follow along using files downloadable from the authors website

Trend of Tobacco Use and Exposure to Secondhand Smoke Among Students Aged 13-15 Years in India and Selected Countries of the South-East Asia Region

Trend of Tobacco Use and Exposure to Secondhand Smoke Among Students Aged 13-15 Years in India and Selected Countries of the South-East Asia Region

Analysis of Pipeline Steel Corrosion Data From NBS (NIST) Studies Conducted Between 1922-1940 and Relevance to Pipeline Management.

Between 1911 and 1984, the National Bureau of Standards (NBS) conducted a large number of corrosion studies that included the measurement of corrosion damage to samples exposed to real-world environments. One of these studies was an investigation conducted between 1922 and 1940 into the corrosion of bare steel and wrought iron pipes buried underground at 47 different sites representing different soil types across the Unites States. At the start of this study, very little was known about the corrosion of ferrous alloys underground. The objectives of this study were to determine (i) if coatings would be required to prevent corrosion, and (ii) if soil properties could be used to predict corrosion and determine when coatings would be required. While this study determined very quickly that coatings would be required for some soils, it found that the results were so divergent that even generalities based on this data must be drawn with care. The investigators concluded that so many diverse factors influence corrosion rates underground that planning of proper tests and interpretation of the results were matters of considerable difficulty and that quantitative interpretations or extrapolations could be done “only in approximate fashion” and attempted only in the “restricted area” of the tests until more complete information is available.Following the passage of the Pipeline Safety Improvement Act in 2002 and at the urging of the pipeline industry, the Office of Pipeline Safety of the U.S. Department of Transportation approached the National Institute of Standards and Technology (NBS became NIST in 1988) and requested that the data from this study be reexamined to determine if the information handling and analysis capabilities of modern computers and software could enable the extraction of more meaningful information from these data. This report is a summary of the resulting investigations.The data from the original NBS studies were analyzed using a variety of commercially available software packages for statistical analysis. The emphasis was on identifying trends in the data that could be later exploited in the development of an empirical model for predicting the range of expected corrosion behavior for any given set of soil chemistry and conditions. A large number of issues were identified with this corrosion dataset, but given the limited knowledge of corrosion and statistical analysis at the time the study was conducted, these shortcomings are not surprising and many of these were recognized by the investigators before the study was concluded. However, it is important to keep in mind that complete soil data is provided for less than half of the sites in this study. In agreement with the initial study, it was concluded that any differences in the corrosion behavior of the alloys could not be resolved due to the scatter in the results from the environmental factors and no significant difference could be determined between alloys. Linear regression and curve fitting of the corrosion damage measurements against the measured soil composition and properties found some weak trends. These trends improved with multiple regression, and empirical equations representing the performance of the samples in the tests were developed with uncertainty estimates. The uncertainties in these empirical models for the corrosion data were large, and extrapolation beyond the parameter space or exposure times of these experiments will create additional uncertainties.It is concluded that equations for the estimation of corrosion damage distributions and rates can be developed from these data, but these models will always have relatively large uncertainties that will limit their utility. These uncertainties result from the scatter in the measurements due to annual, seasonal, and sample position dependent variations at the burial sites. The data indicate that more complete datasets with soil property measurements reflecting the properties of the soil and ground water directly in contact with the sample from statistically designed experiments would greatly reduce this scatter and enable more representative predictions.

Daily blood pressure profile in young subjects based on Student Health Center data

Objective. To assess the incidence of different variants of arterial hypertension (AH) among students daily BP regulation. Design and methods. 234 students of Stavropol State Medical Academy of both sexes aged 18-23 years (middle age 21,4  0,2 years) were examined, among them 73 men and 161 women. Physical examination and blood pressure (BP) measuring by traditional and monitor methods were performed. For statistical analysis software package STATISTICA, version 6,0 was used. Results. 10,7 % (25 people) of students had isolated offi ce AH and 11,5 % (27 people) had latent AH. Subjects having any type of labile AH demonstrated an increase of daily systolic and diastolic BP and an elevation of mean hemodynamic and pulse BP that has negative predictive value, they also showed an increase of pathological daily profi le rate. Conclusions. About a quarter of young people have various forms of AH that can be diagnosed by only use of 24-hour BP monitoring. They need an active follow-up at the centers of students health.

Missing Data Imputation in Time Series of Air Pollution

ISEE-0841 Background and Objective: Missing data is a frequent problem in epidemiological studies on the effects of air pollution on health. Air quality monitoring stations can present failures and stay off-line for several days. These gaps can distort the exposure assessment since the missing data mechanism is often ignored in the analysis. Analyses based only on the available observations can yield biased estimates of the association as well as overestimate the precision. We propose an imputation procedure for multivariate time series data, e.g. daily concentrations of atmospheric contaminants, based on the EM algorithm. The time component of the series can be modelled by using splines, regression models, or ARIMA models with multiple covariance regime. Methods: A simulation study was carried out in order to evaluate the validity of the proposed method and compare it with those available as default in most software packages for statistical analysis. The accuracy and agreement of the methods were also evaluated and a penalty criterion due to the lost information was proposed in order to account for the imputation uncertainty in the analysis. Results: (i) data analysis using only the complete units tended to underestimate the association between the air contaminants and the health events even when only a small amount of data is missing; (ii) mean and median imputation overestimated the association and estimates show high dispersion and low agreement between when compared to the original values; (iii) multivariate procedures presented better performance and accuracy than the univariate ones; (iv) multivariate methods with temporal adjustment presented higher accuracy and precision. This last approach also presented smaller prediction error and higher agreement between the imputed and the original values. Conclusion: The methods proposed in this work are implemented as an open source library called mtsdi for the statistical software R.

PROC QTL—A SAS Procedure for Mapping Quantitative Trait Loci

Statistical analysis system (SAS) is the most comprehensive statistical analysis software package in the world. It offers data analysis for almost all experiments under various statistical models. Each analysis is performed using a particular subroutine, called a procedure (PROC). For example, PROC ANOVA performs analysis of variances. PROC QTL is a user-defined SAS procedure for mapping quantitative trait loci (QTL). It allows users to perform QTL mapping for continuous and discrete traits within the SAS platform. Users of PROC QTL are able to take advantage of all existing features offered by the general SAS software, for example, data management and graphical treatment. The current version of PROC QTL can perform QTL mapping for all line crossing experiments using maximum likelihood (ML), least square (LS), iteratively reweighted least square (IRLS), Fisher scoring (FISHER), Bayesian (BAYES), and empirical Bayes (EBAYES) methods.

Statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data.

statnet is a suite of software packages for statistical network analysis. The packages implement recent advances in network modeling based on exponential-family random graph models (ERGM). The components of the package provide a comprehensive framework for ERGM-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC) algorithm. The coding is optimized for speed and robustness.

Application of immunomagnetic cell enrichment in combination with RT‐PCR for the detection of rare circulating head and neck tumor cells in human peripheral blood

Detection of rare, circulating tumor cells (CTC's) in human peripheral blood is a potential indicator of prognosis and diagnosis in oncology. Typical methods to detect these CTC's are either by immunocytochemistry (ICCS) or RT-PCR. However without accurate, rapid, and reproducible enrichment processes, these detection techniques are labor intensive and/or unreliable. In this article, a repeatable enrichment process that included a flow-through immunomagnetic cell separation system, the quadrupole magnetic sorter (QMS) was optimized with the aid of a statistical analysis software package. The QMS was operated in a negative mode of operation by immunomagnetically targeting normal human peripheral blood lymphocytes (PBL) through the CD45 surface marker. Three head and neck squamous carcinoma cell lines (HNSCC), Detroit-562, SCC-4, and CAL-27, were used to determine the sensitivity of RT-PCR for the epidermal growth factor receptor (EGFR) in spiked PBL. The detection purity needed for detection was found to be one cell in 10(4), one cell in 10(3), and one cell in 10(5) for the Detroit-562, SCC-4, and CAL-27, respectively. The actual number of cancer cells needed for RT-PCR detection ranged from 30 to 1 cell. To mimic the potential concentration of rare CTC present in peripheral blood of cancer patients, the spiking concentration was chosen to be one cancer cell per 10(5) total leukocytes from healthy donors. Using a single step immunomagnetic labeling, the final, optimized enrichment process produced a 57.6 +/- 30.3-fold (n = 6) enrichment of the rare cancer cells with a final cancer cell recovery of (77.8 +/- 6.6)%.

The Signature from Messenger RNA Expression Profiling Can Predict Lymph Node Metastasis with High Accuracy for Non-small Cell Lung Cancer

The extent of regional lymph node (LN) metastasis is the most important factor in the evaluation of resectability and prognosis of non-small cell lung cancer (NSCLC) to increase the chance of complete cure. The authors attempted to deduce a group of genes from the analysis of mRNA expression profiles of the tumor tissues of NSCLC patients with or without LN metastasis, and make a classification model for better prediction of LN metastasis. The authors analyzed mRNA expression profiles of 79 NSCLC patients with or without LN metastasis, and deduced the gene signature for the predictive model of LN metastasis in lung cancer. The authors evaluated the predictive accuracy of each of four algorithms by applying them to another set of 33 NSCLC patients. Each algorithm's accuracy was calculated by 10-fold cross-validation, and a combined model showed a level of accuracy that was higher than any one of the better three component algorithms (i.e., ANN, DT, or NB). Avadis, SAS, ArrayXPath, and R-package were the statistical analysis software packages used. The authors selected 949 genes using a classical permutation t test (p < 0.01) and finally obtained a gene signature consisting of 31 genes by adjustment of multiple-hypothesis testing. The LN metastasis prediction model derived from the signature (31 genes) and their characteristic interactions provided a predictive accuracy of 84.85% when applied to a test set of 33 patients. The authors have demonstrated that their gene signature developed by the expression profiling of mRNAs from the primary tissue could predict the LN metastasis status of NSCLC.

WE‐C‐ValA‐07: Statistical Analysis of Respiratory Motion and Knowledge Discovery

Purpose: Quantified characterization and better understanding of tumor respiratory motion is valuable for understanding of respiration, motion‐included treatment planning, online prediction, and real‐time control algorithm for dose delivery in image guided radiotherapy. There are two goals of this work: (1) to discover the correlation among various motion variables so that we can understand patient respiratory better and (2) to build an analytical system for online motion modeling and prediction during real‐time treatment delivery. Method and Materials: Statistical analysis of tumor respiratory motion has been performed over 48 real patient data. Quantified information of different motion characteristics, including amplitude, frequency, velocity and the mean positions are computed over different granularities. Sample granularities include a breathing state, a breathing cycle, a treatment session, a patient and the whole patient population. Association rules among different motion characteristics are mined and formulated. Results: We have implemented the software packages for statistical analysis and correlation presentation. Quantified motion information have been computed and displayed. The spatio‐temporal changes of these properties are studied. Knowledge of respiratory motion and the underlying physiological explanation have been exploited. The probability distribution functions of various correlations among different properties have been calculated and visualized. Conclusion: Different statistical analyses over a set of tumor motion characteristics have shown that there are some general rules regarding tumor motion. The analytical results help us to obtain new knowledge and to understand the physiological actions of tumor motion and to treatment moving tumor more efficiently. Conflict of Interest:

Generalized Rank Tests for Replicated Microarray Data

Gene expression data from microarray experiments have been studied using several statistical models. Significance Analysis of Microarrays (SAM), for example, has proved to be useful in analyzing microarray data. In the spirit of the SAM procedures, we develop permutation based rank-tests for generalized Wilcoxon ranksum test for two-group comparisons of replicated microarray data. Also, for microarray experiments with randomized block design, we consider generalized signed rank test. The statistical analysis software package is written in R and is freely available in a package.

Utilizing commodity hardware and software to distribute a real-world application: maximizing reuse while improving performance

Commodity computing hardware continues to increase performance while decreasing price. This combination is driving a renewed interest in parallel and distributed computing. In this study, we examine the performance of an existing application in a ten-node computing cluster using commodity off-the-shelf components. The application is a statistical analysis software package that processes categorical data used by state public safety programs. The study examines various network topologies and focuses on minimizing the software modifications required to distribute the application. We conclude that parallel computing using commodity components is an effective mechanism to increase the performance of real-world applications especially when the underlying application architectures have the flexibility to support efficient reuse of the existing code. Copyright © 2005 John Wiley & Sons, Ltd.

An Introduction toStatCrunch 3.0

StatCrunch, formerly known as WebStat, is a statistical data analysis software package for the World Wide Web (WWW). After version 1.0 [1] of the software was published as a prototype package in 1997, version 2.0 was released in 1999 as a fully functional statistical software package. Version 2.0 offered an easy-to-use interface and basic statistical routines which were well suited for educational and low level research needs. The main aim of the menu driven package was to offer a freely accessible alternative to commercial statistical software via the WWW with the only minimal requirement on the part of the user being a Java capable Web browser such as Internet Explorer or Netscape Navigator.

Taking up binge drinking in college: the influences of person, social group, and environment

Purpose To identify person, social group, and environmental factors associated with uptake of binge drinking among a national sample of college students. Methods Using self-reported responses of students in the 1999 Harvard School of Public Health College Alcohol Study (CAS), we regressed conceptually important predictors of binge drinking onto a dichotomized variable describing uptake in the freshman year. This was a random sample of full-time undergraduates provided by the registrar at each participant school ( n = 119). For this study, we analyzed data describing a subset of the total sample comprising first year students aged ≤19 years, excluding transfers ( n = 1894). The student CAS is a 20-page voluntary, anonymous mailed questionnaire containing student reports about their alcohol and substance use, school activities, and background characteristics. Analyses included univariate and multivariate logistic regression adjusting for school response rate and using the Generalized Estimating Equations (GEE) in the Statistical Analysis Software package to handle the within-college clustering owing to the sampling scheme. Results College students who reported that they were exposed to “wet” environments were more likely to engage in binge drinking than were their peers without similar exposures. Wet environments included social, residential, and market surroundings in which drinking is prevalent and alcohol cheap and easily accessed. Findings held up in multivariate analyses that included variables describing person and social group characteristics. Students who picked up binge drinking in college also were more likely than their peers to report inflated definitions of binge drinking and more permissive attitudes about appropriate ages for legal consumption. Conclusions Binge drinking can either be acquired or avoided in college among students who report they did not binge drink in high school. Reducing college binge uptake may require efforts to limit access/availability, control cheap prices, and maximize substance free environments and associations.

KyPlot — A User-oriented Tool for Statistical Data Analysis and Visualization

KyPlot is a software package for statistical data analysis and visualization. The software is directed at end-users in various research fields. It is intended to provide an integrated environment in which the user can enter, manipulate and analyze data with a broad range of statistical methods on a spreadsheet interface, and visualize the results as versatile graphs. The software package has been distributed via the Internet and the development of the software has proceeded through communications with the users via the Internet.

Low incidence of human papillomavirus type 16 antibody seroconversion in young children.

Low incidence of human papillomavirus type 16 antibody seroconversion in young children.

Survey of Software with Mixture Experiment Capabilities

Vendors of statistical experimental design and data analysis software packages were contacted to gather information on software having mixture experiment capabilities. A total of 15 packages having mixture experiment capabilities were identified. The mixture experiment design capabilities, data analysis capabilities, and miscellaneous information about the software (e.g., type of interface, hardware requirements, and cost) are summarized in table form. Mailing addresses, email addresses, phone numbers, and fax numbers of the software vendors are also provided.

Adoption of Lower-fat Food Choices by Americans

Nutrition messages targeting consumption of lower-fat food choices (i.e., use of skim milk, lean meats and/or fat-modified products) have been disseminated to consumers using the population approach. The current level of adoption of these food choices, and the demographics of adopters, are unknown. This study uses the 1989–90, 1990–91, and 1991–92 Continuing Survey of Food Intake by Individuals and the Statistical Analysis Software (SAS) package to address these issues and test the applicability of Roger's Diffusion of Innovations theory to lower-fat food choices. There was a significant relationship between year and milk use (p< .001), year and meat use (p< .01), year and product use (p< .001), and year and overall strategy use (p< .001). For milk users, use of skim milk increased from 8.5% in 1989 to 12.8% in 1992, while use of higher-fat milks decreased from 87.4% to 80.8%. Use of fat-modified cheese, pudding, salad dressing, cake, and yogurt increased (3.4% to 4.2%) and use of full-fat products decreased (91.4% to 87.7%) when comparing the first and third years. Conversely, use of lean meats decreased (3.4% to 3.1%) and use of higher-fat meats increased (67.9% to 70.7%) for these years. Overall use of any one strategy increased from 10.5% to 12.6%, and use of more than one strategy increased from 0.7% to 1.4%. In general, adoption rates from 1989–92 were small, indicating that Americans are currently in the early stages of adopting these lower-fat food choices. Skim milk users were more likely to be older, more educated, and Caucasian when compared to users of higher-fat milks (p <.0l). Users of lean meats and fat-modified products were more likely to be older than higher-fat meats users (p <.01) and full-fat products users (p< .01). Users of more than one strategy were more likely to be older, more educated, and female when compared to users of none or one strategy (p <.01). Diffusion of Innovations studies consistently report that early adopters have higher levels of education and income. This study identifies alternative and additional factors, such as age, ethinicity, and gender, that relate specifically to early adoption of lower-fat food choices.

X-ray studies, empirical, semi-empirical and statistical calculations on a series of thyrotropin-releasing hormone derivatives

The crystal structure of two thyrotropin-releasing hormone (TRH) derivatives has been determined and used with previous X-ray structures of other analogs as the starting set for in vacuo semi-empirical and empirical calculations, in order to evaluate the conformational freedom of these compounds. Data collected from the dynamics calculations have been used for attempting a classification of the preferred arrangements by means of a statistical analysis software package. Consensus dynamics calculations have also been performed to extrapolate a pharmacophore model.

An adaptable microcomputer program for monitoring behavior

We describe an improvement of a flexible and easy to use program for monitoring and recording behavior durations with IBM-compatible microcomputers and a companion program that provides additional versatility for handling data. Up to 35 behaviors or events can be monitored, and processing speed is fast enough to measure even very rapidly performed behaviors. No specialized hardware is required, and the data files that are created are compatible with commercially available software packages for statistical analysis.