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Results for “Data Interpretation, Statistical”

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At least 19 recordsLinked to original sources
PubMed1999

[Cardiovascular risk in menopause: myth, paradox or reality. The importance of clinical observations vs statistical data interpretation].

Coronary mortality among men is higher than in women in all age groups. Women seem to be protected from coronary disease. After the age of 50, coronary mortality increases fast in women and slows down in men, so that the mortality curves tend to join at some level. This fact led to believe that menopause had some influence over the non-proportional increase of coronary risk in women. Estrogens would be responsible for this protection before menopause and their further decrease would be the cause of higher coronary mortality. Mortality rate data, taken from a population with a high rate disease or a low rate disease, do not prove the above statement. For this reason it is said to by a myth. If it were a myth, then to try to prevent by administrating a substitute of these estrogens during menopause would be a paradox. If we accept that coronary atherosclerosis disease has many etiological factors, we should conclude that, besides hormones, there are other elements involved. The kind of personality, the way of life together with genetic factors related to gender would explain differences in frequency and mortality due to coronary heart disease, which, in this case, clearly advocates for women.

Adult↗
PubMed1980

Quality-control statistical interpretation by microcomputer.

A comprehensive summary report of quality-control data is of great value in monitoring the accuracy and precision of the clinical chemistry laboratory. This report allows a retrospective appraisal alerting laboratory personnel to possible test control material degradation or instrument malfunction. A microcomputer-based program package is described, designed to reduce the errors and lengthy preparation inherent in the manual generation of such a report. Quality-control summary data is automatically compared with a predefined set of statistical criteria, and any aberrant values are flagged, thus eliminating subjective and nonuniform data interpretation. Statistical comparisons include: number of control points, delta mean, delta standard deviation, standard deviation index, and F-ratio. The summary report is well accepted by the laboratory staff, and its incorporation into the decision-making process allows for an efficient, critical, and uniformly rigorous examination of analytic proficiency.

Chemistry, Clinical↗
PubMed2004

A critique of Miller and Rohling's statistical interpretive method for neuropsychological test data.

A critical review of the 24-step procedure of Miller and Rohling's (in press) proposed standardization of clinician's use of neuropsychological assessment batteries is presented. Each step is examined for statistical sources of invalidity. It was concluded that parts of the procedure are quite vulnerable to between-battery variability that cannot be easily estimated or controlled, leading to significant errors in analysis and classification. A second fatal flaw is the failure to distinguish in the procedures between standard error measurement and standard error of the estimate in calculations in several steps. The purpose of the process remains viable, however, and is an important contribution toward the improvement of clinical diagnosis.

Algorithms↗
PubMed2000

[Categorizing variables in the statistical analysis of data: consequences for interpreting the results].

Frequently during the process of data analysis in epidemiological studies, the scale of one or more continuous variables is changed. The objective of this paper was to assess the consequences of categorizing variables during data analysis. We studied three situations with different scenarios for statistical analysis with regression models. The results show that dichotomizing continuous variables can substantially modify the relationships between dependent and independent variables. Thus, for example, in epidemiological studies trying to evaluate the effect of an exposure on a response, the magnitude and/or the direction of this effect can be biased by dichotomizing a variable. We therefore recommend avoiding, as much as possible, the categorization of variables when doing analyses.

Analysis of Variance↗
PubMed1999

Interpreting data on thoracic aortic aneurysms. Statistical issues.

Reliable information on growth rates and risk factors for growth of thoracic aortic aneurysms (TAA) is important for managing patients with this potentially lethal condition. This article reviews existing procedures for ascertaining TAA growth rates and describes improved statistical methodologies. Using data from the Yale Center for Thoracic Aortic Disease, the article demonstrates that the statistical procedure of instrumental variables (IV) estimation leads to substantially more precise and robust estimates of TAA growth rates and associated risk factors. We recommend that IV estimation be routinely employed in estimating the progression of thoracic aortic aneurysms and in identifying risk factors for growth. The article also discusses the issue of sample selection effects that arise when patients receive graft surgery and therefore are removed from the data set, and describes statistical procedures fro addressing this issue.

Aortic Dissection↗
PubMed2026

A Guide for Exploring Pleiotropic Associations in Genome-Wide Association Studies Using Summary Statistics.

Genome-wide association studies (GWAS) have shown that pleiotropy, whereby a single genetic variant or gene influences multiple traits, is common in complex human diseases. Detecting cross-phenotype associations from GWAS summary statistics remains challenging because of small effect sizes, extensive multiple testing, heterogeneous effects, and possible differences in effect direction across traits. Methods that jointly analyze multiple traits can improve the ability to detect pleiotropic signals while retaining the practical advantages of summary statistic-based analyses. Although a range of statistical approaches has been developed for this purpose, practical guidance on their application, assumptions, and interpretation remains limited. This tutorial reviews several widely used methods for pleiotropy detection from GWAS summary statistics, including ASSET, PLACO, GPA, CPBayes, and GCPBayes, and demonstrates their application using breast and thyroid cancer datasets. We also highlight the importance of accounting for effect heterogeneity, correlation, and biological group structure at the gene and pathway levels in the detection and interpretation of pleiotropic association signals.

Genome-Wide Association Study↗
PubMed2006

New additivity schemes for hydrocarbon energies.

[structure: see text] A new isodesmic additivity scheme based on the energetic relationships among the simplest hydrocarbon molecules reproduces the experimental heats of formation for a broad range of unstrained hydrocarbons with remarkable accuracy. The stabilizations of radicals, double, and triple CC bonds by alkyl substituents (hyperconjugation), as well as the stabilization by 1,3-alkyl group interactions at the same carbon (branching), support conventional interpretations. Statistical data fitting can also be achieved by using only four adjustable parameters.

Journal Article↗
PubMed2002

[Statistical tests: value, principle and interpretation].

Comparison of data groups requires the use of statistical tests. This article utilizes an example taken from the Revue Médicine Tropicale to illustrate the value of statistical testing as well as an interpretation of the probability value "p" associated with test results.

Data Interpretation, Statistical↗
PubMedDate not supplied

Can't miss: conquer any number task by making important statistics simple. Part 6. Tests of statistical significance (z test statistic, rejecting the null hypothesis, p value), t test, z test for proportions, statistical significance versus meaningful difference.

Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data. This article, Part 6, merges the four concepts of the (1) standard error of the difference between sample means, (2) the z test statistic, (3) rejecting the null hypothesis, and (4) the p value to provide a comprehensive view of tests of statistical significance. This is followed by a description of t tests, statistical tests for comparing two sample proportions, and Type I and Type II errors. The series of articles concludes with a description of statistical significance versus meaningful difference.

Confidence Intervals↗
PubMedDate not supplied

CAN'T MISS--conquer any number task by making important statistics simple. Part 2. Probability, populations, samples, and normal distributions.

Healthcare quality improvement professionals need to understand and use inferential statistics to interpret sample data from their organizations. In quality improvement and healthcare research studies all the data from a population often are not available, so investigators take samples and make inferences about the population by using inferential statistics. This three-part series will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals for samples of data. This article, Part 2, describes probability, populations, and samples. The uses of descriptive and inferential statistics are outlined. The article also discusses the properties and probability of normal distributions, including the standard normal distribution.

Data Interpretation, Statistical↗