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At least 811 records · Page 45Linked to original sources
PubMed1997

Overcoming feelings of powerlessness in "aging" researchers: a primer on statistical power in analysis of variance designs.

A general rationale and specific procedures for examining the statistical power characteristics of psychology-of-aging empirical studies are provided. First, 4 basic ingredients of statistical hypothesis testing are reviewed. Then, 2 measures of effect size are introduced (standardized mean differences and the proportion of variation accounted for by the effect of interest), and methods are given for estimating these measures from already-completed studies. Power and sample size formulas, examples, and discussion are provided for common comparison-of-means designs, including independent samples I-factor and factorial analysis of variance (ANOVA) design, analysis of covariance designs, repeated measures (correlated samples) ANOVA designs, and split-plot (combined between- and within-subjects) ANOVA designs. Because of past conceptual differences, special attention is given to the power associated with statistical interactions, and cautions about applying the various procedures are indicated. Illustrative power estimations also are applied to a published study from the literature. It is argued that psychology-of-aging researchers will be both better informed consumers of what they read and more "empowered" with respect to what they research by understanding the important roles played by power and sample size in statistical hypothesis testing.

Aged↗
PubMed2003

The coalescent and infinite-site model of a small multigene family.

The infinite-site model of a small multigene family with two duplicated genes is studied. The expectations of the amounts of nucleotide variation within and between two genes and linkage disequilibrium are obtained, and a coalescent-based method for simulating patterns of polymorphism in a small multigene family is developed. The pattern of DNA variation is much more complicated than that in a single-copy gene, which can be simulated by the standard coalescent. Using the coalescent simulation of duplicated genes, the applicability of statistical tests of neutrality to multigene families is considered.

Data Interpretation, Statistical↗
PubMedDate not supplied

Data analysis for repeated measures studies.

Many research questions in nursing can best be answered by designing studies using repeated measures representing pretreatment and posttreatment observations on the same subject. In this article, the author discusses both a parametric statistical test and its nonparametric counterpart and provides guidance regarding when it is appropriate to use each. The steps used in computing both statistics are also provided.

Chi-Square Distribution↗
PubMed2005

Feature selection and nearest centroid classification for protein mass spectrometry.

BACKGROUND: The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the "curse of dimensionality" needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry. RESULTS: This study examines the performance of the nearest centroid classifier coupled with the following feature selection algorithms. Student-t test, Kolmogorov-Smirnov test, and the P-test are univariate statistics used for filter-based feature ranking. From the wrapper approaches we tested sequential forward selection and a modified version of sequential backward selection. Embedded approaches included shrunken nearest centroid and a novel version of boosting based feature selection we developed. In addition, we tested several dimensionality reduction approaches, namely principal component analysis and principal component analysis coupled with linear discriminant analysis. To fairly assess each algorithm, evaluation was done using stratified cross validation with an internal leave-one-out cross-validation loop for automated feature selection. Comprehensive experiments, conducted on five popular cancer data sets, revealed that the less advocated sequential forward selection and boosted feature selection algorithms produce the most consistent results across all data sets. In contrast, the state-of-the-art performance reported on isolated data sets for several of the studied algorithms, does not hold across all data sets. CONCLUSION: This study tested a number of popular feature selection methods using the nearest centroid classifier and found that several reportedly state-of-the-art algorithms in fact perform rather poorly when tested via stratified cross-validation. The revealed inconsistencies provide clear evidence that algorithm evaluation should be performed on several data sets using a consistent (i.e., non-randomized, stratified) cross-validation procedure in order for the conclusions to be statistically sound.

Algorithms↗
PubMed1992

Home health care classification.

This article is the second in a series on the Home Health Care Classification research study conducted at Georgetown University School of Nursing (see the March 1992 CARING). The purpose of the study was to develop a method to assess and classify home health Medicare patients in order to predict their need for nursing and other home care services, including their outcomes of care. To accomplish this goal, data on actual resource use that could be objectively measured were used to predict resource requirements. A third article will provide more detail on the descriptive findings, statistical analyses, and the Preliminary Classification System.

Aged↗
PubMed1999

Analysis of quantitative research data: Part 1.

Developments in practice should be based on evidence from reliable and rigorous research studies. Nurses must engage with research, even if they do not need to carry it out. Nurses need to be able to appraise critically both text reports of research studies and the statistical analysis that underpins the findings.

Clinical Nursing Research↗
PubMed1991

Statistical conclusion validity. Multiple inferences in rehabilitation research.

The problem of multiple statistical inferences and Type I error rates in rehabilitation research is examined. The Bonferroni method is the most commonly advocated procedure to control Type I error in clinical research. The traditional Bonferroni method is often overly conservative and results in a loss of statistical power when more than a small number of comparisons are evaluated. Adjustments to the Bonferroni method designed to control or reduce the incidence of Type I errors and improve the statistical conclusion validity of rehabilitation research are presented. The adjusted or sharpened Bonferroni methods allow the researcher to control the incidence of Type I errors while maintaining statistical power. Adjustments to the Bonferroni method are simple to compute and applicable to a wide variety of statistical tests. The use of appropriate multiple comparison procedures will reduce the number of Type I errors and improve the statistical conclusion validity of rehabilitation research studies.

Bias↗
PubMed1991

Statistical significance and fragility criteria for assessing a difference of two proportions.

This paper compares the traditional methods of statistical inference on the data from biomedical studies with a proposed index of fragility in the results. In general, for any given study there are 8 possible combinations of conclusions regarding statistical significance, quantitative significance and fragility. The 8 possibilities are considered in turn with respect to how studies in each group might be interpreted. Numerical examples show that not all 8 possibilities need be attainable with a given study design, and that the relative likelihood of them occurring can vary widely. It is concluded that the fragility index may be a useful adjunct to conventional statistical inference, with certain intuitive appeal, but that more empirical experience is needed with the fragility method.

Analysis of Variance↗
PubMed2006

Misuses of correlation and regression analyses in orthodontic research: the problem of mathematical coupling.

INTRODUCTION: The aim of this article was to encourage good practice in the statistical analyses of orthodontic research data. Our objective was to highlight the statistical problems caused by mathematical coupling (MC) in correlation and regression analyses. These statistical problems are among the most common pitfalls in orthodontic research when exploring associations among clinical variables. This article will show why these problems arise and how they can be avoided and overcome. METHODS: Four orthodontic journals were electronically and manually searched for articles that used correlation and regression analyses. Studies that seemed to suffer from MC in their statistical analyses were identified and carefully examined. RESULTS: Several examples from our search illustrate that MC in correlation and regression analyses can potentially cause misleading results. More appropriate statistical methods are available and should be used to eliminate confusing results and improve any subsequent interpretations. Because many clinical and radiographic variables used in orthodontic research are correlated due to direct or indirect MC, interpretation of studies in the literature needs to be cautious. CONCLUSIONS: Correlation and regression analyses are useful tools in orthodontic research when their assumptions and limitations are recognized. However, greater care is required in formulating research questions and experimental designs. It is prudent to seek statistical advice when orthodontic research involves complex data analyses.

Analysis of Variance↗
PubMed2004

Collinearity in linear regression is a serious problem in oral health research.

The aim of this article is to encourage good practice in the statistical analysis of dental research data. Our objective is to highlight the statistical problems of collinearity and multicollinearity. These are among the most common statistical pitfalls in oral health research when exploring the relationship between clinical variables using multiple regression analysis. We hope that this article will show why these problems arise and how they can be avoided and overcome. Examples from the periodontal literature will be used to illustrate how collinearity and multicollinearity can seriously distort the model development process as a result of the phenomenon of mathematical coupling. Knowledge of these problems can help to eliminate misleading results and improve any subsequent interpretations. Regression analyses are useful tools in oral health research when their limitations are recognized. However, care is required in planning and it is worthwhile seeking statistical advice when formulating the study's research questions.

Algorithms↗
PubMed1989

The interpretation of equivocal or marginal animal carcinogenicity tests.

The interpretation of animal carcinogenicity tests traditionally rely almost exclusively upon a comparison of specific tumor rates in treated vs. matched and, perhaps, historical control animals. Yet, carcinogenicity tests yield much more biological and pathological data than simply final tumor rates. This additional data should also be considered as part of the total weight of evidence, particularly when analyzing a marginal or equivocal test result. If there are no positive findings among the data discussed here and listed in Table 1, it is unlikely that a marginal or equivocal increase in tumor incidence is actually treatment-related, irrespective of statistical analysis.

Animals↗