Longitudinal Data Analysis of Symptom Score Trajectories Using Linear Mixed Models in a Clinical Trial

Authors

  • C. Engel Center for Pediatric Clinical Studies, Biometry University Children’s Hospital Tuebingen, Frondsbergstraße 23, 72070 Tübingen, Germany
  • C. Meisner Department of Clinical Epidemiology and Applied Biometry, University of Tuebingen, Silcherstraße 5, 72076 Tübingen, Germany
  • A. Wittorf Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstraße 14, 72076 Tübingen, Germany
  • W. Wölwer Department of Psychiatry and Psychotherapy, University of Düsseldorf, Bergische Landstraße 2, 40629 Düsseldorf, Germany
  • G. Wiedemann Department of Psychiatry and Psychotherapy, Klinikum Fulda gAG, Pacelliallee 4, 36043 Fulda, Germany
  • C. Ring Institute of Epidemiology and Medical Biometry, University of Ulm, Schwabstraße 13, 89075 Ulm, Germany
  • R. Muche Institute of Epidemiology and Medical Biometry, University of Ulm, Schwabstraße 13, 89075 Ulm, Germany
  • S. Klingberg Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstraße 14, 72076 Tübingen, Germany

DOI:

https://doi.org/10.6000/1929-6029.2013.02.04.7

Keywords:

longitudinal studies, randomized controlled trial, linear models, sample size

Abstract

In clinical trials, longitudinal data are often analyzed using T-tests, anovas or ancovas instead of the more powerful linear mixed models. The purpose of this paper is to demonstrate how the more sophisticated linear mixed models according to the approach of Singer and Willett, which allows special insight into the behaviour of the data, can be used in clinical trials. Individual trajectories of PANNS-MNS Scores from a controlled clinical trial were used to demonstrate all the steps needed for an analysis of longitudinal data. The model is built step by step, model assumptions are checked, time-variant and time-invariant factors are included and the results are interpreted. The unique needs of a clinical trial, such as the calculation of effect sizes or of an appropriate sample size, are taken into account. Finally, a flow chart is presented that would serve as an instruction tool for the analysis of longitudinal data in clinical trials.

Author Biographies

C. Engel, Center for Pediatric Clinical Studies, Biometry University Children’s Hospital Tuebingen, Frondsbergstraße 23, 72070 Tübingen, Germany

Center for Pediatric Clinical Studies, Biometry

C. Meisner, Department of Clinical Epidemiology and Applied Biometry, University of Tuebingen, Silcherstraße 5, 72076 Tübingen, Germany

Department of Clinical Epidemiology and Applied Biometry

A. Wittorf, Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstraße 14, 72076 Tübingen, Germany

Department of Psychiatry and Psychotherapy

W. Wölwer, Department of Psychiatry and Psychotherapy, University of Düsseldorf, Bergische Landstraße 2, 40629 Düsseldorf, Germany

Department of Psychiatry and Psychotherapy

G. Wiedemann, Department of Psychiatry and Psychotherapy, Klinikum Fulda gAG, Pacelliallee 4, 36043 Fulda, Germany

Department of Psychiatry and Psychotherapy

C. Ring, Institute of Epidemiology and Medical Biometry, University of Ulm, Schwabstraße 13, 89075 Ulm, Germany

Institute of Epidemiology and Medical Biometry

R. Muche, Institute of Epidemiology and Medical Biometry, University of Ulm, Schwabstraße 13, 89075 Ulm, Germany

Institute of Epidemiology and Medical Biometry

S. Klingberg, Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstraße 14, 72076 Tübingen, Germany

Department of Psychiatry and Psychotherapy

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2013-10-31

How to Cite

Engel, C., Meisner, C., Wittorf, A., Wölwer, W., Wiedemann, G., Ring, C., Muche, R., & Klingberg, S. (2013). Longitudinal Data Analysis of Symptom Score Trajectories Using Linear Mixed Models in a Clinical Trial. International Journal of Statistics in Medical Research, 2(4), 305–315. https://doi.org/10.6000/1929-6029.2013.02.04.7

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