Structural time series models in medicine.
Structural time series models are formulated in terms of components, such as trends, seasonals and cycles, which have a direct interpretation. This article describes such models and gives examples of how they can be applied in medicine. Univariate models are considered first, and then extended to include explanatory variables and interventions. Multivariate models are then shown to provide a framework for modelling longitudinal data and for carrying out intervention analysis with control groups. The final sections deal with data irregularities and non-Gaussian observations.
Harvey
A
A
Department of Statistics, London School of Economics, UK.
Koopman
S J
SJ
D015982:Bias; D015190:Blood Glucose Self-Monitoring / Q000706:statistics & numerical data; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003625:Data Collection; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D008432:Mathematical Computing; D008457:Measles / Q000453:epidemiology; D015233:Models, Statistical; D015999:Multivariate Analysis; D009519:New York City / Q000453:epidemiology; D016011:Normal Distribution; D012622:Seat Belts / Q000706:statistics & numerical data; D006113:United Kingdom