Abstract
Health problems are often idiosyncratic in nature and therefore require individualized diagnosis and treatment. In this paper, we show how single-case experimental designs (SCEDs) can meet the requirement to find and evaluate individually tailored treatments. We give a basic introduction to the methodology of SCEDs and provide an overview of the available design options. For each design, we show how an element of randomization can be incorporated to increase the internal and statistical conclusion validity and how the obtained data can be analyzed using visual tools, effect size measures, and randomization inference. We illustrate each design and data analysis technique using applied data sets from the healthcare literature.
Highlights
Health problems are often idiosyncratic in nature and require individualized diagnosis and treatment
The steps involved in conducting a randomized single-case experimental designs (SCEDs) and analyzing the obtained data with a randomization test are explained in Heyvaert and Onghena [47] and Tanious et al [48]
We showed how randomized SCEDs can be utilized in healthcare research to find individually tailored interventions
Summary
“Averaging data across many subjects can hide a multitude of sins: The experimental treatment may fail to affect the behavior of some subjects, and may even lead to contrary effects in others. Perone highlights the individuality of each person when it comes to finding an effective treatment, which by definition limits the applicability of large-scale group studies for situations in which symptoms are highly idiosyncratic in nature Following this logic, a shift towards individualized testing and experimental results in healthcare followed the accumulation of evidence that general healthcare diagnoses and interventions often fail to accurately describe and relieve patient symptoms (e.g., [2,3,4]). In Turk’s view, the remedy to this myth is a better matching of empirical data to patient characteristics in order to design individual treatment plans These limits of group studies, in healthcare, were recognized in clinical practice after decades, in which they were thought to be the gold standard and the “N-of-1 randomized controlled trial” was included among the highest levels of evidence in the Oxford Centre for Evidence-Based. For each applied data set, we explain stepwise how an element of randomization can be implemented and how the obtained data can be analyzed using visual analysis, effect size calculation, and randomization tests
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
