Applications of time-series analysis: a case study on the impact of computer tomography

Evaluating Disease Management Program Effectiveness: An Introduction to Time-Series Analysis

Currently, the most widely used method in the disease management (DM) industry for evaluating program effectiveness is referred to as the "total population approach." This model is a pretest-posttest design, with the most basic limitation being that without a control group, there may be sources of bias and/or competing extraneous confounding factors that offer a plausible rationale explaining the change from baseline. Furthermore, with the current inclination of DM programs to use financial indicators rather than program-specific utilization indicators as the principal measure of program success, additional biases are introduced that may cloud evaluation results. This paper presents a non-technical introduction to time-series analysis (using disease-specific utilization measures) as an alternative, and more appropriate, approach to evaluating DM program effectiveness than the current total population approach.

Box-Jenkins modelling in medical research

Notifications of diseases, entries in a hospital, injuries due to accidents, etc., are frequently collected in fixed equally spaced intervals. Such observations are likely to be dependent. In environmental medicine, where series such as daily concentrations of pollutants are collected and analysed, it is evident that dependence of consecutive measurements may be important. A high concentration of a pollutant today has a certain 'inertia', i.e. a tendency to be high tomorrow as well. Dependence of consecutive observations may be equally important when data such as blood glucose are recorded within a single patient. ARIMA models (autoregressive integrated moving average models, Box-Jenkins models), which allow the stochastic dependence of consecutive data to be modelled, have become well established in such fields as economics. This article reviews basic concepts of Box-Jenkins modelling. The methods are illustrated by applications. In particular, the following topics are presented: the ARIMA model, transfer function models (assessment of relations between time series) and intervention analysis (assessment of changes of time series).