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Abstract：
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.
摘要：
模拟研究调查了局部线性趋势模型的数据比较精度之间的关系，基线数据变异性，以及引入N-of-1干预后水平和斜率的变化。绘制等高线地图，其中包括基线数据的可变性，改变水平或坡度，以及局部线性趋势模型的状态和预测值之间不重叠数据的百分比。模拟结果表明，基线数据的变异性以及干预后水平和斜率的变化会影响基于局部线性趋势模型的数据比较精度。现场研究采用局部线性趋势模型对实际现场数据进行干预效果调查,这证实了以前的N-of-1研究的100%有效性。这些结果表明，使用局部线性趋势模型，基线数据变异性会影响数据比较的准确性，可以准确预测干预效果。局部线性趋势模型有助于评估精准康复中有效个性化干预的干预效果。
