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. 2022;2022(SI3):10.1162/99608f92.6c21dab7.
doi: 10.1162/99608f92.6c21dab7. Epub 2022 Sep 8.

An R Shiny App for a Chronic Lower Back Pain Study, Personalized N-of-1 Trial

Thevaa Chandereng  1
Affiliations

An R Shiny App for a Chronic Lower Back Pain Study, Personalized N-of-1 Trial

Thevaa Chandereng. Harv Data Sci Rev. 2022.

Abstract

The call for personalized medicine highlights the need for personalized (N-of-1) trials to find what treatment works best for individual patients. Conventional (between-subject) randomized controlled trials (RCT) yield effects for the 'average patient,' but a personalized trial administers all treatments within-subject, so benefits or harms to the individual patient can be identified. The design and analysis of personalized trials involve different strategies from the conventional RCT. These include how to adjust for any carryover effects from one intervention to another, how to handle missing data, and how to provide patients with insight into their data. In addition, a comprehensible report about trial results should be created for each patient and their clinician to facilitate their decision-making. This article describes strategies to address these design and analytic issues, and introduces an R shiny app to facilitate their solution, to explain the use of each of the design and statistical strategies. To illustrate, we also provide a concrete example of a personalized trial series designed to increase activity (i.e., walking steps) in patients with chronic lower back pain (CLBP).

Keywords: computing platforms; fitbit data; imputation; personalized medicine.

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Figures

Figure 1.
Figure 1.. Flowchart of the treatment assignments.
Figure 2.
Figure 2.. Forest plot comparing the difference in the total number of steps (per day) after imputation.
The horizontal line being on the right or left side of the vertical line (in the middle) without intersection indicates the β^>0β^>0 and β^<0β^<0 respectively and the 95% CI of β^β^ does not include 0. On the other hand, the horizontal line intersecting the vertical line indicates that the 95% CI of β^β^ does include 0.
Figure 3.
Figure 3.. A screenshot of the R Shiny app that reports all the analyses for the step count data from the CLBP trial for each patient.
See this image and copyright information in PMC

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