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Biomedical subjects

B J Collings

Publications and source records attributed to B J Collings.

5 recordsLinked to original sources
PubMed1994

Developing an optimal scoring system with a special emphasis on volleyball.

Scoring systems for two-team (two-person) net games are typically evaluated for accuracy, efficiency, and variability. This evaluation may be difficult if many points are involved, the probability of winning a point changes with service, and/or points are not scored every rally. A computer program that may be used to evaluate general scoring systems for accuracy, efficiency, variability, and expected score difference is presented. Four scoring systems, side-out, quick, side-out point, and bonus point, are analyzed. A list of items to consider when developing an optimal scoring system is suggested. An optimal scoring system for volleyball is introduced.

Humans↗
PubMed1988

Estimating the power of the two-sample Wilcoxon test for location shift.

Traditional methods for calculating the power of a statistical test for location shift require knowledge of the shape of the underlying probability distribution. The distribution shape, however, may be unknown. This paper describes a bootstrap method for using observed data (or pilot data) to approximate the power. No assumptions need be made about the shape of the underlying continuous probability distribution. Simulation evidence shows that, when applied to the Wilcoxon two-sample test for location shift, the suggested method is reliable. The evidence also shows that it is more accurate than a benchmark traditional approach. The bootstrap method is applied to a real-data example. The analysis demonstrates how the method can be used to determine sample sizes and how to choose the more powerful of two alternative tests for location shift.

Animals↗
PubMed1983

Statistical analysis and sample-size determinations for mutagenicity experiments with binomial responses.

Two statistical analyses are studied for their applicability to mutagenicity experiments that produce binomial responses from a control group and a single treated group. Attention is focused on experiments with (1) group sample sizes greater than 500 and (2) a probability less than .05 for a binary observation from any experimental unit being "positive." In addition, it is assumed that historical control data will not be included in the statistical analysis. The first analysis is a conditional binomial test, which has been tabulated extensively by Kastenbaum and Bowman [1970], while the second is based on a standard normal approximation to the distribution of the difference between two sample proportions. A formula is presented for each analysis that relates the associated probability of detecting a mutagen to the mutant frequencies and sample sizes of the two groups. Based on extensive numerical results, the conclusion is drawn that the normal test is the preferred analysis for experiments in which the ratio of the two sample sizes is between 0.80 and 1.25. On the further assumption that an experiment is to be conducted with equal experimental group sample sizes, recommendations are offered for values of this common sample size needed to achieve a specified power, ie, a degree of assurance of detecting a postulated level of mutagenic effect.

Animals↗
PubMed1981

Analyses for binomial data, with application to the fluctuation test for mutagenicity.

The fluctuation test proposed by Green, Muriel and Bridges (1976, Mutation Research 38, 33-42), a short-term microbial test for mutagenicity, yields binomial observations for which the probability of success varies with the background mutation rate, the total number of microbial growth cycles for which a microbe is at risk of mutation, and the rate of induced mutation for the compound being tested. A standard one-tailed two-sample binomial test is preferable to the two-tailed test adopted by Green et al. for analyzing data from a control versus single positive dose fluctuation test. Based on exact power computations, recommendations are offered for the design of such a fluctuation test. A simple method of guarding against the impact of a small number of aberrant observations in a multisample binomial problem is studied; it is applicable to the fluctuation test when the protocol involves replicate measurements . Finally, the case of more than one positive dose of the test compound is investigated. Two statistical tests for this situation, both extensions of one-tailed two-sample test, are extensively compared. A departure from monotonicity at high doses has a more serious effect on the power of the 'regression' test than on that of the 'isotonic' test. A variant of the isotonic test, based on the angular transformation, should be avoided.

Microbial Sensitivity Tests↗