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A MISAPPLICATION OF FACTOR ANALYSIS.
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[STUDIES ON IMPROVING THE PERCENTILE SCORES OF VARIOUS IMPORTANT MEDICAL REPORTS AND COMMUNICATIONS THROUGH CONSTANT, CORRECTIVE REVISION].
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The implementation of factor analysis for the evaluation of selected blood parameter changes induced by hyperbaric exposure.
This paper discusses the application of factor analysis when used to compare selected blood parameter (a three-parameter smear, hematocrit, C3c, C4, IgG, IgA, IgM, CRP, fibrinogen and the level of factor XII) properties, just before, and after exposure to pressure changes, and 24-hours after the completion of decompression. To-date the most popular method of statistical analysis was based only on investigation of the significance of the separated individual parameters. This factor analysis that has not been applied previously in the analysis of such problems, enabled the neutral hierarchic evaluation of the significant parameter changes within their chosen range, and mutual relationships. It seems that the application of this method is purposeful and it can be an objective tool for evaluating the significance of changes in blood constituency induced by pressure.
Factor analysis of plasma lipoprotein components.
Serum phospholipids were analyzed for their content of long-chained fatty acids together with other components of lipoproteins (total- and HDL-cholesterol, apolipoproteins A-I and B, triglycerides), in 60 coronary heart disease patients and 30 control individuals. Some of the individual variations in content of the various components showed co-variation with each other. This formed the basis for the extraction of 7 'factors' by the statistical procedure 'factor analysis'. Analysis of variance was performed with the 'factor scores' for subgroups of high and low age and high and low total serum cholesterol. The analysis revealed two unexpected factors which discriminated with statistical significance between young, hypercholesterolaemic patients and controls. One factor was a positive risk factor and the other a negative one. They could possibly be dependent on the existence of two at present uncharacterized subgroups of lipoproteins. These lipoproteins contained, according to the analysis, large amounts of certain fatty acids. It is suggested that fatty acid analysis might be useful in the characterization of lipoproteins that are involved in the development of atherosclerosis.
A statistical perspective on gene expression data analysis.
Rapid advances in biotechnology have resulted in an increasing interest in the use of oligonucleotide and spotted cDNA gene expression microarrays for medical research. These arrays are being widely used to understand the underlying genetic structure of various diseases, with the ultimate goal to provide better diagnosis, prevention and cure. This technology allows for measurement of expression levels from several thousands of genes simultaneously, thus resulting in an enormous amount of data. The role of the statistician is critical to the successful design of gene expression studies, and the analysis and interpretation of the resulting voluminous data. This paper discusses hypotheses common to gene expression studies, and describes some of the statistical methods suitable for addressing these hypotheses. S-plus and SAS codes to perform the statistical methods are provided. Gene expression data from an unpublished oncologic study is used to illustrate these methods.
Three-way component analysis: principles and illustrative application.
Three-way component analysis techniques are designed for descriptive analysis of 3-way data, for example, when data are collected on individuals, in different settings, and on different measures. Such techniques summarize all information in a 3-way data set by summarizing, for each way of the 3-way data set, the associated entities through a few components and describing the relations between these components. In this article, 3-mode principal components analysis is described at an elementary level. Guidance is given concerning the choices to be made in each step of the process of analyzing 3-way data by this technique. The complete process is illustrated with a detailed description of the analysis of an empirical 3-way data set.
Systematic interpretation of microarray data using experiment annotations.
BACKGROUND: Up to now, microarray data are mostly assessed in context with only one or few parameters characterizing the experimental conditions under study. More explicit experiment annotations, however, are highly useful for interpreting microarray data, when available in a statistically accessible format. RESULTS: We provide means to preprocess these additional data, and to extract relevant traits corresponding to the transcription patterns under study. We found correspondence analysis particularly well-suited for mapping such extracted traits. It visualizes associations both among and between the traits, the hereby annotated experiments, and the genes, revealing how they are all interrelated. Here, we apply our methods to the systematic interpretation of radioactive (single channel) and two-channel data, stemming from model organisms such as yeast and drosophila up to complex human cancer samples. Inclusion of technical parameters allows for identification of artifacts and flaws in experimental design. CONCLUSION: Biological and clinical traits can act as landmarks in transcription space, systematically mapping the variance of large datasets from the predominant changes down toward intricate details.
Development of factor-score-based models to explain and predict maximal box-lifting performance.
The objectives of the study were threefold: (1) to develop factor-score-based models to predict maximum mass on a box-lifting task using multiple regressions; (2) to compare predictive and explanatory powers of factor-score-based models to models derived from data-level variables; and (3) to apply these findings to ergonomic research and practical problem-solving situations. Forty-eight volunteers (25 women and 23 men) completed a maximal box-lifting task and a maximal isoinertial lifting test on an Incremental Lifting Machine (ILM). Dynamic data collected during isoinertial testing were summarized into 32 lift parameters, and then subjected to principal components analyses using the 'FACTOR PROCEDURE' from the Statistical Analysis System (SAS). Factor scores were calculated for each participant on each of the four factors comprising the final solution, and multiple regression equations for men, women and combined data were generated using the 'GENERAL LINEAR MODELS' procedure from SAS. Results revealed that prediction of box-lifting performance was optimized when regression equations were developed using numerous data-level variables as predictors, i.e., all 32 lift parameters and ILM mass. In comparison, explanation was enhanced but predictive capabilities were reduced when linear models were formed using ILM mass and the factor scores derived from analyses of isoinertial lifting. The use of variables loading on the factors gave slightly increased predictive power than did the factor-score-based models. Similar trends in predictive and explanatory powers appeared when the data were analysed according to gender. Ergonomic applications of factor-score-based models were discussed with regard to ongoing research as well as to practical problem-solving situations. It was concluded that the advantages and usefulness of factor-score-based models warranted their inclusion in future investigations of lifting performance.
Q methodology--a journey into the subjectivity of human mind.
AIMS: This paper introduces a relatively new research methodological tool, known as Q method, useful in exploring issues related to human subjectivity. DESCRIPTION: Q methodology is unique as it combines the strengths of both qualitative and quantitative research traditions. The sequential steps involve generation of ideas about the research topics, clarification and refinement of the ideas, and rank ordering these ideas by the respondents in a quasinormal distribution. The data are extracted with by-person factor analysis and useful in exploring arrays of attitude either cross-sectionally or longitudinally over a period of time. CONCLUSION: Q methodology can be used to analyse opinions, perceptions, and attitudes in both clinical and non-clinical settings. It is a preferred method of human subjectivity study as it provides more in-depth analysis of complex subjectivity issues.
How many patients are needed to provide reliable evaluations of individual clinicians?
PURPOSE: The purpose of this study was to determine how many patients are needed to provide reliable patient ratings of care at the individual clinician level. SETTING AND SOURCES OF DATA: The study was conducted in an academic medical center and was based on analysis of 34,985 patients who completed a 50-item survey rating the care received during a recent outpatient visit to a physician or midlevel provider. STUDY DESIGN: Analyses of patient satisfaction surveys was done to: 1) confirm the dimensions of satisfaction with outpatient care in an existing measure, and 2) determine the number of patients required to provide reliable estimates of clinician care for single items and an 11-item composite scale. PRINCIPAL FINDINGS: Factor analysis showed that the survey measured 2 dimensions of satisfaction: 1) clinician care, and 2) features of visiting the office. The 11-item clinician care scale had high reliability (Cronbach's alpha=0.97). The number of patients needed to achieve reliability of 0.80 at the clinician level was 66 for the 11-item scale and ranged from 52 to 91 for individual items. For primary care physicians only, the comparable number of patients per clinician was 77 for the 11-item scale and ranged from 50 to 147 across items. CONCLUSIONS: For the survey items that we analyzed, the answer to the question "How many patients are needed to obtain useful and reliable feedback?" is at least 50, but varies by item type (global vs. specific) and by number of items (composite scale or single-item rating) and by the conditions of use (for self-assessment and learning or reward and punishment).
Statistical methodology: VIII. Using confirmatory factor analysis (CFA) in emergency medicine research.
How many underlying characteristics (or factors) does a set of survey questions measure? When subjects answer a set of self-report questions, is it more appropriate to analyze the questions individually, to pool responses to all of the questions to form one global score, or to combine subsets of related questions to define multiple underlying factors? Factor analysis is the statistical method of choice for answering such questions. When researchers have no idea beforehand about what factors may underlie a set of questions, they use exploratory factor analysis to infer the best explanatory model from observed data "after the fact." If, on the other hand, researchers have a hypothesis beforehand about the underlying factors, then they can use confirmatory factor analysis (CFA) to evaluate how well this model explains the observed data and to compare the model's goodness-of-fit with that of other competing models. This article describes the basic rules and building blocks of CFA: what it is, how it works, and how researchers can use it. The authors begin by placing CFA in the context of a common research application-namely, assessing quality of medical outcome using a patient satisfaction survey. They then explain, within this research context, how CFA is used to evaluate the explanatory power of a factor model and to decide which model or models best represent the data. The information that must be specified in the analysis to estimate a CFA model is highlighted, and the statistical assumptions and limitations of this analysis are noted. Analyzing the responses of 1,614 emergency medical patients to a commonly-used "patient satisfaction" questionnaire, the authors demonstrate how to: 1) compare competing factor-models to find the best-fitting model; 2) modify models to improve their goodness-of-fit; 3) test hypotheses about relationships among the underlying factors; 4) examine mean differences in "factor scores"; and 5) refine an existing instrument into a more streamlined form that has fewer questions and better conceptual and statistical precision than the original instrument. Finally, the role of CFA in developing new instruments is discussed.
The effect of indomethacin on heterotopic ossification following acetabular fracture surgery.
Sixty-six patients with acetabular fractures requiring a posterior or extensile surgical approach were treated using a uniform protocol that was begun in July 1984. In 1987, prophylactic Indomethacin was added to the protocol to study its effects on the prevention of heterotopic ossification. Forty-six patients were operated on prior to and 20 after the initiation of the Indomethacin treatment program. Patients were observed for at least 6 months and the incidence in severity of heterotopic ossification between groups was compared using the Brooker classification. Patients' were also evaluated for associated risk factors. Statistical analysis revealed that the male sex and the extensile approach were the significant risk factors (p < 0.01). Evaluation of drug treatment revealed that while Indomethacin was not effective in completely eliminating heterotopic ossification, the occurrence of severe heterotopic ossification (Brooker Class III and Class IV) was significantly reduced (p < 0.05).
[Development of an experimental methodology: hypotheses and variables].
In this methodological paper, we present the elaboration of the experimental design, central stage of the research cycle. First, we focus on the hypothesis elaboration from the research question. Then, we detail the hypothesis operationalisation by the means of the choice of variables. Finally, the experimental controls and the factorial designs are presented.
Imince: an unrestricted factor-analysis-based program for assessing measurement invariance.
In this article, a Windows program for analyzing measurement invariance in two different populations is described. Factor analysis is a common way of assessing measurement invariance, and restricted factor analysis is now the most popular method. However, applied researchers have usually found that the theoretical advantages of restricted factor analysis do not always apply in practical situations. For example, when the participant sample is large, as is the case in Internet-based questionnaires, the available software for restricted factor analysis might fail to converge on a solution. Our program is based on unrestricted factor analysis and considers the three parameters that define factor invariance: difficulties, discriminations, and residual variances. The statistical significance of the tests for evaluating invariance is obtained using Bootstrap resampling procedures. A real-life example demonstrates the usefulness of the program.
Is there a Gulf War syndrome?
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[Risk factors for the occurrence of nephrolithiasis in the Aral Sea region].
The effects of environmental hazards on nephrolithiasis onset were studied for population of the regions exposed to ecological catastrophe sequelae by the sea Aral. Blood and urine levels of organic acids and trace elements were measured using chromato-mass spectrometry, absorption plasma spectrophotometry and ion-exchange chromatography, respectively, as well as urinary peptide hydrolases activity in 178 patients with nephrolithiasis. The levels of lithogenic substances in the blood and urine were distributed differently in patients living in different ecological zones. The above ecologically detrimental zones were denoted as zone 1--most distant from the Aral, zone 2--less distant from it and zone 3--the most ecologically damaged regions of the Aral catastrophe. The multivariate statistical analysis revealed factors of risk to develop nephrolithiasis for these zones: high and moderate risk was stated for 26%, 28.5% and 42.5% of the patients from zones 1, 2 and 3, respectively. The findings confirm the conception on an essential role of environmental factors in initiation of Aral-region nephrolithiasis.
The impact of young drivers' lifestyle on their road traffic accident risk in greater Athens area.
Young drivers (18-24) both in Greece and elsewhere appear to have high rates of road traffic accidents. Many factors contribute to the creation of these high road traffic accidents rates. It has been suggested that lifestyle is an important one. The main objective of this study is to find out and clarify the (potential) relationship between young drivers' lifestyle and the road traffic accident risk they face. Moreover, to examine if all the youngsters have the same elevated risk on the road or not. The sample consisted of 241 young Greek drivers of both sexes. The statistical analysis included factor analysis and logistic regression analysis. Through the principal component analysis a ten factor scale was created which included the basic lifestyle traits of young Greek drivers. The logistic regression analysis showed that the young drivers whose dominant lifestyle trait is alcohol consumption or drive without destination have high accident risk, while these whose dominant lifestyle trait is culture, face low accident risk. Furthermore, young drivers who are religious in one way or another seem to have low accident risk. Finally, some preliminary observations on how health promotion should be put into practice are discussed.