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1.
Chinese Journal of Epidemiology ; (12): 1159-1163, 2019.
Article in Chinese | WPRIM | ID: wpr-797788

ABSTRACT

Interrupted time-series (ITS) is a quasi-experimental design which evaluates the effectiveness of an intervention based on time-series outcome variables. Compared with the single group of ITS, the two groups of ITS can better control the influence of pre-interventional confounding factors and evaluate the effectiveness of the intervention. This paper summarizes the principles and statistical methods of two groups of ITS by an example of evaluating vaccine effect on the incidence of a disease in two cities. The regression model is fitted by Prais-Winsten method and Newey-West method and the results are explained and compared in detail. When the intervention is performed with other confounding interventions at the same time, the two groups of ITS can be more effective to balance the existing trends before the intervention, and evaluate the effectiveness of intervention. The method of two groups of ITS has important practical significance, providing new insights in program evaluation.

2.
Chinese Journal of Preventive Medicine ; (12): 858-864, 2019.
Article in Chinese | WPRIM | ID: wpr-810870

ABSTRACT

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.

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