PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Data Interpretation, Statistical”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16Linked to original sources
PubMedDate not supplied

Helping students learn and apply statistical analysis. A metacognitive approach.

Metacognition, the ability to decipher known from unknown information continuously, can facilitate nursing students' study of statistical analysis. By giving learners the "big picture" about critical issues in quantitative data analysis, faculty members can help students use statistics knowledgeably. Faculty members can use devices such as the chart contained in this article to lower student anxiety and assist with the real-world application of statistical analysis.

Data Interpretation, Statistical↗
PubMed2004

Research in a dental practice setting.

There is a shortage of research from dental practice. The aim of this article is to stimulate more interest in dental research. This is done by explaining the basic principles of doing research in a dental practice setting. Examples are taken from the author's own practice. Emphasis is placed on the following points: how to develop and research ideas; factors specific to dental practice; how articles and journals are rated; making a protocol for the study; examiners' reliability and statistical analysis.

Child↗
PubMed1994

[Critical comments on the statistical methods in Grawe, Donati and Bernauer: "Change in psychotherapy. From confession to profession"].

In the meta-analysis "Changing Psychotherapy" by Grawe, Donati and Bernauer different psychotherapeutic methods are compared based upon published therapy studies. Hereby the authors claim also to have proven with statistical methods that certain kinds of therapy are more effective than others. I show here that the descriptive and inductive methods used are not able to withstand a critical examination; they are incorrect and in most cases even inadmissible. The results of my examination show that there are four points of critique: 1. The question of how effective a kind of therapy is, according to Grawe's criteria, depends more on the number of variables and their measurements with which a therapy is judged than on the number of patients examined in the single studies. 2. Grawe does not distinguish between dependent and independent variables or measurements; every measurement of each variable is included in his methods with the same weight. 3. The different effect variables used to evaluate the therapic process are mostly represented on varying ordinal scales which are incomparable with each other. Grawe treats these scales as if they were comparable, often even as if they were metric. 4. All five statistical methods (counting significances, binomial test, profile of difference values, t-test, Wilcoxon-test) with which Grawe evaluates the results of the single studies are inadmissible because the conditions required are not met. In sum: The conclusions stated in the meta-analysis cannot be seen as being statistically validated or statistically proven.

Bias↗
PubMed2005

Explorative statistical analysis of planar point processes in microscopy.

Basic methods of explorative statistical analysis for stationary and isotropic planar point processes are briefly and informally reviewed. At the explorative level, planar point patterns may be characterized in terms of the intensity, the K-function and the pair correlation function. These second-order functions enable one to classify a given point process as completely random, clustering or repulsive. The repulsive behaviour may be quantified by an estimate of the hard-core distance. In the exploratory approach, the statistics are essentially free from model assumptions. Second-order spatial functions have been estimated to characterize genuine planar point processes in the macroscopic domain, for example in forestry, geography and epidemiology. For light microscopy and transmission electron microscopy, two situations are distinguished, which may be summarized as the genuine planar case and the stereological case. In the genuine planar case, a direct interpretation of the results of spatial statistics is feasible. Here, monolayers in cell culture, intramembranous particles on freeze fracture specimens and amacrine cells of the retina are mentioned as examples. In the stereological case, point patterns are generated by sections through 3D structures. Here the observed point patterns may arise as the centres of sectional profiles of particles, or as centres of sectional profiles of spatial fibre processes. In both situations, exploratory spatial point process statistics allow a quantitative characterization of sectional images for the purposes of group comparisons and classification. Moreover, for spatial fibre processes it has recently been shown that the observed pair correlation function of the centres of the fibre profiles is an estimate of the reduced pair correlation function of the fibre process in 3D. Hence for fibre processes a stereological interpretation of point process statistics obtained from sections is an additional option.

Data Interpretation, Statistical↗
PubMed2005

Finding the fittest fold: using the evolutionary record to design new proteins.

For many years, the holy grail of protein engineering has been the design of artificial amino acid sequences that fold into stable proteins with desired functions. In the current issue of Nature, two papers from the Ranganathan group (Russ et al., 2005; Socolich et al., 2005) report remarkable success in the design of artificial WW domains. Their method, termed statistical coupling analysis (Lockless and Ranganathan, 1999), does not use structural or physicochemical information but instead extracts information about essential patterns of amino acids from the evolutionary record.

Amino Acid Sequence↗
PubMed2000

Subgroup analysis and other (mis)uses of baseline data in clinical trials.

BACKGROUND: Baseline data collected on each patient at randomisation in controlled clinical trials can be used to describe the population of patients, to assess comparability of treatment groups, to achieve balanced randomisation, to adjust treatment comparisons for prognostic factors, and to undertake subgroup analyses. We assessed the extent and quality of such practices in major clinical trial reports. METHODS: A sample of 50 consecutive clinical-trial reports was obtained from four major medical journals during July to September, 1997. We tabulated the detailed information on uses of baseline data by use of a standard form. FINDINGS: Most trials presented baseline comparability in a table. These tables were often unduly large, and about half the trials inappropriately used significance tests for baseline comparison. Methods of randomisation, including possible stratification, were often poorly described. There was little consistency over whether to use covariate adjustment and the criteria for selecting baseline factors for which to adjust were often unclear. Most trials emphasised the simple unadjusted results and covariate adjustment usually made negligible difference. Two-thirds of the reports presented subgroup findings, but mostly without appropriate statistical tests for interaction. Many reports put too much emphasis on subgroup analyses that commonly lacked statistical power. INTERPRETATION: Clinical trials need a predefined statistical analysis plan for uses of baseline data, especially covariate-adjusted analyses and subgroup analyses. Investigators and journals need to adopt improved standards of statistical reporting, and exercise caution when drawing conclusions from subgroup findings.

Bias↗