Abstract

The T-pattern model and its extensions, called the T-system, with corresponding detection algorithms and the software, THEME^TM (see patternvision.com), were developed to facilitate detection of recurrent patterns in records of time stamped (behavioral) events. While statistical analysis of such data have typically focused on the directly recorded (coded) recurrent categories, for example, their frequency and duration, this approach introduces an intermediate step where new recurrent “categories” (patterns) are first discovered that in turn can be counted, measured and analyzed, using standard statistical methods often to discover effects of independent variables otherwise missed. This more in-depth approach may also provide practical advantages sometimes needing fewer samples and/or subjects for the detection of effects. While analysis of voluminous data is possible with Theme, the particular use of temporal (realtime) information also allows analysis of tiny data (even just a few events) as typically recorded in short interactions common in clinical work and also from the new smartwatch data collection application called ThemeWatch described below. The T-pattern model with some of its extensions and corresponding Theme algorithms including the new Theme retro-and prediction features is described. Through an illustrative T-pattern detection example and references to numerous studies using this approach, the purpose of this paper is thus to suggest some of the possibilities offered by T-pattern Detection and Analysis using the dedicated software, Theme.