Extrapolating baseline trend in single-case data: Problems and tentative solutions

Rumen Manolov, Antonio Solanas, Vicenta Sierra

Producció científica: Article en revista indexadaArticleAvaluat per experts

13 Cites (Scopus)


Single-case data often contain trends. Accordingly, to account for baseline trend, several data-analytical techniques extrapolate it into the subsequent intervention phase. Such extrapolation led to forecasts that were smaller than the minimal possible value in 40% of the studies published in 2015 that we reviewed. To avoid impossible predicted values, we propose extrapolating a damping trend, when necessary. Furthermore, we propose a criterion for determining whether extrapolation is warranted and, if so, how far out it is justified to extrapolate a baseline trend. This criterion is based on the baseline phase length and the goodness of fit of the trend line to the data. These proposals were implemented in a modified version of an analytical technique called Mean phase difference. We used both real and generated data to illustrate how unjustified extrapolations may lead to inappropriate quantifications of effect, whereas our proposals help avoid these issues. The new techniques are implemented in a user-friendly website via the Shiny application, offering both graphical and numerical information. Finally, we point to an alternative not requiring either trend line fitting or extrapolation.

Idioma originalAnglès
Pàgines (de-a)2847-2869
Nombre de pàgines23
RevistaBehavior Research Methods
Estat de la publicacióPublicada - 1 de des. 2019
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