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At least 991 records · Page 55Linked to original sources
PubMed2000

A guide for use and interpretation of kinesiologic electromyographic data.

Physical therapists are among the most common users of electromyography as a method for understanding function and dysfunction of the neuromuscular system. However, there is no collection of references or a source that provides an overview or synthesis of information that serves to guide either the user or the consumer of electromyography and the data derived. Thus, the purpose of this article is to present a guide, accompanied by an inclusive reference list, for the use and interpretation of kinesiologic electromyographic data. The guide is divided into 4 major sections: collecting, managing, normalizing, and analyzing kinesiologic electromyographic data. In the first of these sections, the issues affecting data collection with both indwelling and surface electrodes are discussed. In the second section, data management through alternative forms of data processing is addressed. In the third section, various reasons and procedures for data normalization are discussed. The last section reviews qualitative descriptors once used as the only means of analyzing data, then focuses on more quantitative procedures that predominate today. The guide is intended as a tool for students, educators, clinicians, and beginning researchers who use and interpret kinesiologic electromyographic data. Modifications will likely be needed as alternative forms of collecting, managing, normalizing, and analyzing electromyographic data are proposed, used in various settings, and reported in the literature.

Biofeedback, Psychology↗
PubMed2005

Improving the reporting of clinical case series.

PURPOSE: To describe common errors in the analyses and data presentation of a clinical case series and to suggest simple solutions. DESIGN: Instructional examples. METHODS: Problems with commonly used data analysis and reporting techniques in clinical case series are described using both theoretical examples and those from the literature. RESULTS: An analysis reporting the proportion of a series of patients with variable follow-up does not adequately account for the differential follow-up among patients and is a potentially misleading way to present data. Instead, the proportion of patients at presentation (or study entry) and the rate during follow-up should be reported. Similarly, an analysis in which the final visual acuity of a series of patients with variable follow-up is reported does not adequately account for the effect of time and also may be misleading. Reporting of the rates of visual acuity events during follow-up (e.g., falling below a specified threshold, such as 20/50 or worse) is preferred. Alternatively, when there is nearly complete follow-up, reporting the distribution of visual acuity at specified time points (e.g., 1 year after study presentation) is appropriate. Small case series should not be overinterpreted because of the effects of chance, and appropriate statistical analyses should be employed. CONCLUSIONS: Clinical case series often suffer from several potential reporting flaws. Correction of these flaws would permit the proper interpretation of the data and allow for the ability to combine data from several case series to assemble more meaningful and reliable conclusions.

Case-Control Studies↗
PubMed1993

Influences on inferences. Effect of errors in data on statistical evaluation.

BACKGROUND: Inadvertent random and systemic errors introduced into data sets and manipulation of data are well-defined sources of discrepancies in statistical evaluation of clinical trials. In this study, the authors show the influence of errors on the widely used statistical result, P values. METHODS: Using data from a retrospective study of patients with Hodgkin disease treated at the University of Minnesota between 1970 and 1984 and observed to 1988, we introduced various errors into the data to study the impact on results. RESULTS: Inadvertent random and systemic errors affect statistical results. Data entry and transcription errors, vague definitions of endpoints and prognostic factors, and the omission and selection of patients are examples of frequent errors that affect statistical evaluation. CONCLUSION: The results and inferences of many studies are sensitive to systemic errors and data manipulation. Great care must be given to the clear definitions of terms, exclusion and inclusion criteria, group assignments, treatment protocols, and the subgroups on which statistical analysis is performed. Clinicians and statisticians must work together to improve the performance and interpretation of clinical trials.

Clinical Trials as Topic↗
PubMedDate not supplied

Linking public health data using geographic information system techniques: Alaskan community characteristics and infant mortality.

This article identifies geographical contexts important in differentiating infant mortality in Alaska and uses spatial processing models to link and analyse multi-source data. Information characterizing geographical locations are collected from Alaska's vital statistics for the years 1982-91 and the 1990 Census. Geographic information system (GIS) techniques are applied to identify spatially homogeneous regions, assess spatial compatibility across databases, and allocate geographical units across boundaries. A primary goal of this paper is to encourage spatial linkage and analysis techniques for vital statistics and census data. By demonstrating the interplay of tabular, graphical, and mapping techniques on Alaskan infant mortality, this analysis describes procedures for conducting epidemiological research with data spatially defined at distinct geographical levels.

Alaska↗
PubMed1990

Applications of global statistics in analysing quality of life data.

Quality of life (QOL) instruments usually consist of a number of components, each of which deals specifically with a particular functionally related dysfunction. In a clinical trial whose primary aim is the evaluation of the treatment by means of QOL instruments, analysis of each of the components usually consists of either univariate analysis of variance (ANOVA) or some non-parametric methods. This multiple testing approach can produce an increase in false positive findings. One attempt to correct for this is the Bonferroni adjustment. Another approach is to apply global statistics (parametric or non-parametric) for the null hypothesis of no treatment difference versus the alternative hypothesis that one treatment is uniformly better than the other for QOL instruments as a whole. Data from a randomized double-blind trial of 111 congestive heart failure patients, which involved four QOL instruments, were analysed with univariate ANOVA, Bonferroni adjustment, parametric and non-parametric global statistics. The global statistics complemented the univariate methods and made the presentation of QOL data very effective. I recommend the general use of global statistics in analysis of QOL data.

Attitude to Health↗
PubMed1989

Analysis of quality of life data from a randomized, placebo-controlled heart-failure trial.

In a randomized, double-blind, placebo-controlled, 3-months trial involving 111 congestive heart failure patients, one non-validated and three validated Quality of Life (QL) instruments were administered. Two randomized treatment groups were evaluated, one with 62 patients who continued on standard therapy and the other with 49 patients whose standard therapy was replaced by placebo. The data from Patient's Self-rating Scale (a non-validated instrument) and Spitzer's QL index showed a significant difference between the two treatment groups for an overall effect. There were no significant differences between two treatment groups for Sickness Impact Profile (SIP) and Quality of Well-Being (QWB). For analyzing the multiple components in a QL instrument, the global statistics as suggested by O'Brien were applied to compare the two treatment groups. Univariate statistics complemented the global methods. The general use of global statistics in analyzing QL data is recommended.

Aged↗
PubMed2004

Statistical presentation and analysis of ordinal data in nursing research.

OBJECTIVES: The aim of this study was to review the presentation and analysis of ordinal data in three international nursing journals in 2003. METHOD: In total, 166 full-length articles from the 2003 editions of Cancer Nursing, Scandinavian Journal of Caring Sciences and Nursing Research were reviewed for their use of ordinal data. RESULTS: This review showed that ordinal scales were used in about a third of the articles. However, only about half of the articles that used ordinal data had appropriate data presentation and only about half of the analyses of the ordinal data were performed properly. CONCLUSIONS: Ordinal data are rather common in nursing research, but a large share of the studies do not present/analyse the result properly. Incorrect presentation and analysis of the data may lead to bias and reduced ability to detect statistical differences or effects, resulting in misleading information. This highlights the importance of knowledge about data level, and underlying assumptions for the statistical tests must be considered to ensure correct presentation and analyses of data.

Bibliometrics↗
PubMed2005

Presentation and analysis of radiographic data in clinical trials and observational studies.

Despite the advent of sophisticated imaging systems, plain radiography continues to be a valuable outcome variable in clinical trials of inflammatory disorders for a number of reasons. This paper discusses the pros and cons of the different ways in which radiographic data in trials is presented; the minimum time needed to demonstrate radiographic progression in the context of a clinical trial; and the best ways to statistically analyse radiographic data.

Arthritis, Rheumatoid↗
PubMed1990

[Reflections on information management in clinical research].

Some considerations are made on the importance of clinical, information and its transcendence in medical research, as well as on the ethical value of a qualitatively correct data treatment. After describing by means of a bibliographical search, the present-day tendency towards knowledge based systems, there is offered a general overview of clinical information management, from data collection through study design and statistical assessment, up to the building of a knowledge data base.

Artificial Intelligence↗
PubMed2004

Disease-modifying therapy in MS: a critical review of the literature. Part I: Analysis of clinical trial errors.

Several types of errors are commonly made during the conduct, analysis, and interpretation of clinical trials in multiple sclerosis (MS). These include statistical errors of overestimating the significance of trial results, particularly when multiple endpoints are evaluated. They also include errors arising from the use of inappropriate covariate analyses, meta-analyses, and post hoc subgroup analyses. Interpretation of trial results can also be confounded by regression to the mean, by post hoc data re-analysis, and by the use of a non-concurrent control population. As these kinds of errors continue to plague the medical literature, it has become important for physicians to be able to assess critically the reports of clinical trial results. In turn, this has made it necessary for physicians to become familiar with the rudiments of the fields of statistics, epidemiology, and trial design. It is the purpose of this manuscript, therefore, to provide an overview of these principles through a detailed analysis of these kinds of clinical trial errors, together with examples that have actually occurred in the recently published MS literature.

Bias↗