PubMed · 7973208
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
R G Newcombe. 1994-06-30. Confidence intervals for a binomial proportion.. https://doi.org/10.1002/sim.4780131209
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
A modified version of the 'path finder' display consisting of many small oriented Gabor patches was used to study the joint contributions of spatial and temporal structures to shape perception. A two-interval forced-choice procedure measured detectability of curved 'paths' defined by orientation ('good continuation') and/or by temporal synchrony of change in motion direction ('common fate'). When orientation was completely random (no spatial 'path' cue) temporal synchrony still supported reliable performance, but only when correlation of change among 'path' elements was high. When combined, these two weak spatial and temporal structures yielded performance in excess of probability summation: 'paths' weakly defined by orientation were highly conspicuous when the constituent Gabors underwent synchronized changes in direction of motion, even though the individual directions of path elements were uncorrelated. Spatial grouping from temporal structure may arise from correlated transients associated with synchronized changes in motion direction. Evidently these two mechanisms for promotion of spatial grouping interact synergistically.
We propose a new procedure for constructing inferences about a measure of interobserver agreement in studies involving a binary outcome and multiple raters. The proposed procedure, based on a chi-square goodness-of-fit test as applied to the correlated binomial model (Bahadur, 1961, in Studies in Item Analysis and Prediction, 158-176), is an extension of the goodness-of-fit procedure developed by Donner and Eliasziw (1992, Statistics in Medicine 11, 1511-1519) for the case of two raters. The new procedure is shown to provide confidence-interval coverage levels that are close to nominal over a wide range of parameter combinations. The procedure also provides a sample-size formula that may be used to determine the required number of subjects and raters for such studies.
Sequential tests are increasingly used to reduce the expected sample size of trials in medical research. The majority of such methods are based on the assumption of normality for test statistics. In clinical trials yielding a single sample of discrete data, that assumption is often poorly satisfied. In this paper we show how a novel application of the spending function approach of Lan and DeMets can be used together with exact calculation methods to design sequential procedures for a single sample of discrete random variables without the assumption of normality. A special case is that of binomial data and the paper is illustrated by the design of a cytogenetic study which motivated this work.