gms | German Medical Science

50. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds)
12. Jahrestagung der Deutschen Arbeitsgemeinschaft für Epidemiologie (dae)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie
Deutsche Arbeitsgemeinschaft für Epidemiologie

12. bis 15.09.2005, Freiburg im Breisgau

Internal, External, and Cross-Model Validation of a Multi-Outcome Decision Model for Parkinson's Disease

Meeting Abstract

  • Uwe Siebert - Inst. for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, USA
  • Bernhard Bornschein - Ludwig-Maximilians-Universität, IBE, Munich Program on HTA and Decision Sciences, München
  • Gaby Sroczynski - Inst. for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, USA
  • Richard D. Dodel - Universität Bonn, Klinik für Neurologie, Bonn

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Deutsche Arbeitsgemeinschaft für Epidemiologie. 50. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 12. Jahrestagung der Deutschen Arbeitsgemeinschaft für Epidemiologie. Freiburg im Breisgau, 12.-15.09.2005. Düsseldorf, Köln: German Medical Science; 2005. Doc05gmds569

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/gmds2005/05gmds249.shtml

Veröffentlicht: 8. September 2005

© 2005 Siebert et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Introduction

We have recently reported on a generic, multi-outcome disease model for Parkinson's disease (PD). Now we present first results of internal, external and cross-model validation [1].

Methods

Our lifetime PD Markov model simulates a hypothetical cohort of patients moving through health states reflecting patient characteristics that would be observed in the absence of treatment (Hoehn&Yahr "off" states [HYoff]). We used HYoff I-V and death as Markov states. The model is designed to simultaneously predict multiple outcomes, e.g. time in Hoehn&Yahr “on” states (HYon) observed under treatment, quality-adjusted life expectancy (QALE), or complication rates. As internal validation, we compared time in HYoff stages predicted by our model to results reported in the progression study used to derive our input parameters [2]. As external validation, we compared model results of mean times in HYoff and HYon states with extern literature data not used in our model [3], [4]. Finally, we cross-validated our model comparing QALE under levodopa treatment with QALE of other published models reporting this outcome [5], [6].

Results

Internal validation of HYoff input data showed a 97.4-99.9% accuracy. Although external validation of average HYoff progression rates overestimated external population data from Hoehn & Yahr [7] by 19%, the mean HYon progression rate predicted by our model (0.42 HY stages/y) matched well with estimates reported in the literature (0.40 HY stages/y) [3], [4]. After restricting our model to a 5-year time horizon, discounted QALYs exceeded those from two other published models by 21% and 33% [5], [6]. These differences were mostly attributable to different Markov state-specific utilities. As other Markov models for drug treatment did not evaluate QALE, we could not cross-validate for this outcome.

Conclusions

The Parkinson’s Disease Outcome Model is internally valid and closely reproduces external data for progression under standard treatment. Variability in QALE are due to a combination of different model design, state-specific utilities, and underlying study populations.

Acknowledgement

This study was supported by a research grant from the German Federal Ministry of Education and Research/Parkinson Competence Network, 01GI9901/1).


References

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Weinstein MC, O'Brien BJ, Hornberger J, Jackson J, Johannesson M, McCabe C, et al. Principles of Good Practice for Decision Analytic Modeling in Health-Care Evaluation: Report of the Ispor Task Force on Good Research Practices - Modeling Studies. Value in Health 2003, 6(1): 9-17.
2.
Martilla RJ, Rinne UK. Disability and Progression in Parkinson`S Disease. Acta Neurologica Scandinavia 1977, 56(1977): 159-69
3.
Di Rocco A, Molinari SP, Kollmeier B, Yahr MD. Parkinson's Disease: Progression and Mortality in the L-Dopa Era. Advances in Neurology 1996, 69: 3-11.
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Hely MA, Morris JGL, Traficante R, Reid WGJ, O'Sullivan DJ, Williamson PM. The Sydney Multicentre Study of Parkinson's Disease: Progression and Mortality at 10 Years. Journal of Neurology, Neurosurgery and Psychiatry 1999, 67(3): 300-7
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