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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16252

Title: A permutation solution to test for treatment effects in alternation design single-case experiments
Authors: SOLMI, Francesca
Onghena, Patrick
Salmaso, Luigi
Bulté, Isis
Issue Date: 2014
Citation: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 43 (5), p. 1094-1111
Abstract: Research involving a clinical intervention is normally aimed at testing the treatment effects on a dependent variable, which is assumed to be a relevant indicator of health or quality-of-life status. In much clinical research large-n trials are in fact impractical because the availability of individuals within well-defined categories is limited in this application field. This makes it more and more important to concentrate on single-case experiments. The goal with these is to investigate the presence of a difference in the effect of the treatments considered in the study. In this setting, valid inference generally cannot be made using the parametric statistical procedures that are typically used for the analysis of clinical trials and other large-n designs. Hence, nonparametric tools can be a valid alternative to analyze this kind of data. We propose a permutation solution to assess treatment effects in single-case experiments within alternation designs. An extension to the case of more than two treatments is also presented. A simulation study shows that the approach is both reliable under the null hypothesis and powerful under the alternative, and that it improves the performance of a considered competitor. In the end, we present the results of a real case application.
URI: http://hdl.handle.net/1942/16252
DOI: 10.1080/03610918.2012.725295
ISI #: 000326071600011
ISSN: 0361-0918
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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