title: [Time-series analysis on road traffic injury in China].
abstract: OBJECTIVE
  To establish the predictive models of road traffic injury(RTI) in China, to know the trend of RTI, and to provide the reference data for controlling RTI in China.
  
  
  METHODS
  The China RTI data from 1951 to 2003 were collected, and in view of the problem of missing values, the method of intrapolation was adopted. The Box-Jenkins technique was used to analyze and predict the trend of RTI in China. Following the process for stationary time-series analysis, model identification, parameter estimation and model diagnosis, the predictive equation for RTI would be established.
  
  
  RESULTS
  A series of predictive equations on RTI were finally established based on ARIMA models. The curve fitting is effective and the predictive data of RTI in 2003 are close to the true statistical data.
  
  
  CONCLUSIONS
  The time-series model thus established proves to be of significant usefulness in RTI prediction.
authors.affiliation: Chinese Center for Evidence-Based Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Wen
authors.forename: Ping
authors.initials: P
authors.lastname: Yuan
authors.forename: Zhen-Hua
authors.initials: ZH
authors.lastname: Deng
authors.forename: Kuan-Lin
authors.initials: KL
authors.lastname: Liu
authors.forename: Yue-Kang
authors.initials: YK
authors.lastname: Zhang
authors.forename: Li-Ke
authors.initials: LK
authors.lastname: Liu
authors.forename: Bin
authors.initials: B
authors.lastname: Kong
authors.forename: Si-Xing
authors.initials: SX
authors.lastname: Huang
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D002681:China / Q000453:epidemiology; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D012621:Seasons; D013997:Time Factors