@article{WilbertBoernertRinglebLueke2022,
  author    = {Wilbert, J{\"u}rgen and B{\"o}rnert-Ringleb, Moritz and L{\"u}ke, Timo},
  title     = {Statistical Power of Piecewise Regression Analyses of Single-Case Experimental Studies Addressing Behavior Problems},
  series = {Frontiers in Education},
  volume    = {7},
  journal   = {Frontiers in Education},
  publisher = {Frontiers Media},
  address   = {Lausanne, Schweiz},
  issn      = {2504-284X},
  doi       = {10.3389/feduc.2022.917944},
  pages     = {1 -- 13},
  year      = {2022},
  abstract  = {In intervention research, single-case experimental designs are an important way to gain insights into the causes of individual changes that yield high internal validity. They are commonly applied to examine the effectiveness of classroom-based interventions to reduce problem behavior in schools. At the same time, there is no consensus on good design characteristics of single-case experimental designs when dealing with behavioral problems in schools. Moreover, specific challenges arise concerning appropriate approaches to analyzing behavioral data. Our study addresses the interplay between the test power of piecewise regression analysis and important design specifications of single-case research designs. Here, we focus on the influence of the following specifications of single-case research designs: number of measurement times, the initial frequency of the behavior, intervention effect, and data trend. We conducted a Monte-Carlo study. First, simulated datasets were created with specific design conditions based on reviews of published single-case intervention studies. Following, data were analyzed using piecewise Poisson-regression models, and the influence of specific design specifications on the test power was investigated. Our results indicate that piecewise regressions have a high potential of adequately identifying the effects of interventions for single-case studies. At the same time, test power is strongly related to the specific design specifications of the single-case study: Few measurement times, especially in phase A, and low initial frequencies of the behavior make it impossible to detect even large intervention effects. Research designs with a high number of measurement times show robust power. The insights gained are highly relevant for researchers in the field, as decisions during the early stage of conceptualizing and planning single-case experimental design studies may impact the chance to identify an existing intervention effect during the research process correctly.},
  language  = {en}
}
@article{KonigorskiWernickeSlosareketal.2021,
  author    = {Konigorski, Stefan and Wernicke, Sarah and Slosarek, Tamara and Zenner, Alexander Maximilian and Strelow, Nils and Ruether, Darius Ferenc Ruether and Henschel, Florian and Manaswini, Manisha and Pottb{\"a}cker, Fabian and Edelman, Jonathan Antonio and Owoyele, Babajide and Danieletto, Matteo and Golden, Eddye and Zweig, Micol and Nadkarni, Girish N. and Bottinger, Erwin},
  title     = {StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials},
  series = {Journal of Medical Internet Research},
  volume    = {24},
  journal   = {Journal of Medical Internet Research},
  edition   = {7},
  publisher = {JMIR Publications},
  address   = {Richmond, Virginia, USA},
  issn      = {1438-8871},
  doi       = {10.2196/35884},
  pages     = {12},
  year      = {2021},
  abstract  = {N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice.},
  language  = {en}
}