Interrupted time series (ITS) is a statistical method for the quasi-experimental design specific to the outcome of time series, in which the effectiveness of an intervening measure is evaluated by examining change in slope and immediate change in level. The key feature of ITS is that the secular trend of time series prior to the intervention can be effectively controlled so as to accurately estimate the intervention effect. The design principle and statistical method for ITS were illustrated by an example of evaluating halving policy for the expert registration fee in the general hospital of a city. The segmented linear regression was used to fit the above time series data and the results were explained in detail. Meanwhile, the study design and model fitting along with explanations of the results with respect to the effects of two types of successive interventions and on different time-points of an intervention were illustrated as well in this paper. The existed upward or downward trend should be taken into account in order to accurately estimate the intervention effect as it exists in most of the public health surveillance data. Two parameters, known as change in slope and immediate change in level, were employed to evaluate the effect of the intervention. The ITS analysis can be widely applied to the program evaluation as it could enrich methods of the evaluation compared to the traditional model of the program evaluation.
中断时间序列（ITS）设计是针对时间序列的结果变量，通过检验干预前后斜率改变量和干预点即刻水平改变量，对干预措施的有效性进行评价的设计方法。ITS的特点是在有效地控制干预前已存在的上升或下降趋势后，更准确地估计干预效应。本文以某综合医院专家挂号费减半对门诊量影响为例，阐述单组ITS的设计原理和统计方法，使用分段线性回归拟合模型，并对结果进行解释。同时，阐述先后存在两种干预的效果评价ITS模型和干预后不同时间点效果评价ITS模型的设计原理、模型拟合方法和结果解释。公共卫生监测数据大多都有时间趋势，为了准确估计干预措施的有效性，需要考虑干预前序列已经存在的上升或下降趋势。ITS通过斜率改变量和即刻水平改变量两个指标评价干预效果，丰富了传统的干预评价模式，在干预效果评价中必有广泛的应用。.
Keywords: Interrupted time series design; Intervention studies; Models, statistical.