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Results for “Data Interpretation, Statistical”

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At least 433 records · Page 24Linked to original sources

PubMed1998

Multiple correlations and Bonferroni's correction.

Correlation coefficients between biological measurements and clinical scales are often calculated in psychiatric research. Calculating numerous correlations increases the risk of a type I error, i.e., to erroneously conclude the presence of a significant correlation. To avoid this, the level of statistical significance of correlation coefficients should be adjusted. Threshold levels of significance for correlation coefficients were adjusted for multiple comparisons in a set of k correlation coefficients (k = 1, 5, 10, 20, 50, 100) by Bonferroni's correction. Significant correlation coefficients were then calculated according to sample size. The change in the threshold values of significance is larger when the number of correlations goes from 1 to 5 than when it goes from 50 to 100. A correlation coefficient, statistically significant at 5% when calculated alone, can be under the threshold level of significance when calculated even among a few other coefficients. Focusing on the most relevant variables or the use of multivariate statistics is advocated.

Data Interpretation, Statistical↗

PubMed1989

Interactive morphometric procedures and statistical analysis in the diagnosis of ovarian dysplasia and carcinoma.

We report on our continued experience with an interactive morphometric method recently introduced by us for the definition and diagnosis of ovarian dysplasia vs. normal or malignant epithelium. The main quantitative differences between these three diagnostic categories are based on 1) cytology of the nuclei (nuclear area, circularity factor, maximum chord) and 2) on stratification (distances of nuclear centers to the basement membrane and number of cells per unit length of basement membrane). We implemented our approach on live video images viewed on a monitor overlaid with a touch sensitive screen by one of two interactive procedures: 1) by tracing nuclear profiles (procedure DRAW) or 2) by tracing the basement membrane and touching the center of all nuclei (procedure NU-MEAS). In all cases statistical analysis was performed on a string of multiple variables by stepwise discriminant analysis. Now we have straightened our data basis and are able to obtain diagnosis of unknown samples with very high posterior probabilities. Both procedures are effective but NU-MEAS requires the least effort and seems to give the best statistics.

Cell Nucleus↗

PubMed2004

A graphical approach for quality control of oligonucleotide array data.

In studies of quality control of oligonucleotide array data, one objective is to screen out ineligible arrays. Incomparable arrays (one type of ineligible arrays) arise as the experimental factors are poorly controlled. Due to the high volume of data in gene arrays, examination of array comparability requires special treatments to reduce data dimension without distortion. This paper proposes a graphical approach to address these issues. The proposed approach uses percentile methods to group data, and applies the 2D image plot to display the grouped data. Moreover, an invariant band is employed to quantify degrees of array comparability. We use two publicly available oligonucleotide array datasets from Affymetrix GeneChip System for evaluation. The results demonstrate the utility of our approach to examine data quality and also as an exploratory tool to verify differentially expressed genes selected by vigorous statistical methods.

Algorithms↗

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↗

PubMedDate not supplied

Statistical graphics in pharmacokinetics and pharmacodynamics: a tutorial.

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.

Computer Graphics↗

PubMed2004

[Statistical analyses: hypotheses or proof?].

Marc Girard is a mathematician and a physician, psychotherapist and medical expert witness on pharmaceutical drugs. He tells us his reflexions about the limits of Evidence Based Medicine, and introduces us about a critical view of the use of statistics in scientific medicine.

Bias↗

PubMed1996