Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies

• Published in: Journal of personalized medicine Vol. 13; no. 5; p. 720
• Main Authors: Suzuki, Makoto, Tanaka, Satoshi, Saito, Kazuo, Cho, Kilchoon, Iso, Naoki, Okabe, Takuhiro, Suzuki, Takako, Yamamoto, Junichi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.04.2023; MDPI
• Subjects: Accuracy; Analysis; Behavior; Cellular proteins; Field study; Forecasts and trends; Intervention; Precision medicine; Random variables; Rehabilitation; Research design; Simulation; Variability; N-of-1 trial; local linear trend model; intervention effect; baseline-data variability; precision rehabilitation
• ISSN: 2075-4426; 2075-4426
• DOI: 10.3390/jpm13050720

The simulation study investigated the relationship between the local linear trend model’s data-comparison accuracy, baseline-data variability, and changes in level and slope after introducing the N-of-1 intervention. Contour maps were constructed, which included baseline-data variability, change in level or slope, and percentage of non-overlapping data between the state and forecast values by the local linear trend model. Simulation results showed that baseline-data variability and changes in level and slope after intervention affect the data-comparison accuracy based on the local linear trend model. The field study investigated the intervention effects for actual field data using the local linear trend model, which confirmed 100% effectiveness of previous N-of-1 studies. These results imply that baseline-data variability affects the data-comparison accuracy using a local linear trend model, which could accurately predict the intervention effects. The local linear trend model may help assess the intervention effects of effective personalized interventions in precision rehabilitation.