Extensions of permutation solutions to test for treatment effects in replicated single-case alternation experiments with multivariate response

Single-case experiments are frequently used to do research involving a clinical intervention, since large-n trials are often impractical in clinical research. In order to investigate a possible difference in the effect of the treatments considered in the study, nonparametric instruments are valid tools; in particular, permutation solutions work well when we wish to assess differences in treatment effects. We present an extension of a permutation solution to the multivariate response case and to the case of replicated single-case experiments. A simulation study shows that the approach is both reliable under the null hypothesis and powerful under the alternative. At the end, we present the results of an application to two real experiments. © 2014 Copyright Taylor and Francis Group, LLC.status: publishe

EXTENSIONS OF PERMUTATION SOLUTIONS TO TEST FOR TREATMENT EFFECTS IN REPLICATED SINGLE-CASE ALTERNATION EXPERIMENTS WITH MULTIVARIATE RESPONSE

Single-case experiments are frequently used to do research involving a clinical intervention, since large-n trials are often impractical in clinical research. In order to investigate a possible difference in the effect of the treatments considered in the study, nonparametric instruments are valid tools; in particular permutation solutions work well when we wish to assess differences in treatment effects. We present an extension of a permutation solution to the multivariate response case and to the case of replicated single-case experiments. A simulation study shows that the approach is both reliable under the null hypothesis and powerful under the alternative. At the end we present the results of an application to two real experiments

Analyzing data from single-case alternating treatments designs

Alternating treatments designs (ATDs) have received comparatively less attention than other single-case experimental designs in terms of data analysis, as most analytical proposals and illustrations have been made in the context of designs including phases with several consecutive measurements in the same condition. One of the specific features of ATDs is the rapid (and usually randomly determined) alternation of conditions, which requires adapting the analytical techniques. First, we review the methodologically desirable features of ATDs, as well as the characteristics of the published single-case research using an ATD, which are relevant for data analysis. Second, we review several existing options for ATD data analysis. Third, we propose 2 new procedures, suggested as alternatives improving some of the limitations of extant analytical techniques. Fourth, we illustrate the application of existing techniques and the new proposals in order to discuss their differences and similarities. We advocate for the use of the new proposals in ATDs, because they entail meaningful comparisons between the conditions without assumptions about the design or the data pattern. We provide R code for all computations and for the graphical representation of the comparisons involved. (PsycINFO Database Record