Methods for predictor analysis of repeated measurements: application to psychiatric data

In schizophrenia research, little attention yet has been directed on methods for analyzing data from studies with repeated measurements over time. Motivation for this research stems from a project within the German Research Network on Schizophrenia, in which an algorithm is developed to guide prodrome-based early intervention strategies in stable first episode patients.

We present two different approaches for the analysis of correlated response data, the Generalized Estimating Equations (GEE) method and the Artificial Neural Network (ANN) approach. We illustrate the methods using the data of the A.N.I. study, which is one of the largest German multicenter treatment studies in regard to the long-term treatment of schizophrenia conducted between 1983 and 1989.

The results of statistical model selection prior to GEE analysis and various data presentation methods for ANNs are presented. The primary goal of our evaluation is to investigate if the defined prodromes are valid predictors for relapse. Additionally, it is shown that both methods are applicable on a realistic data set.

It is concluded that both methods are suitable for predictor analysis especially since all variable time points of the patients are included instead of only selected, so that it can be assumed that results are not biased. With the GEE method a test of association for each predictor can be performed whereas with ANNs a general proposition can be made for prodromes depending on the type of data presentation. Using the A.N.I. data the prodrome 'trouble sleeping' seems to be the most informative predictor. Finally, the important differences of the two methods are discussed.