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66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V. (TMF)

26. - 30.09.2021, online

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Comparing strategies for analysing count data in randomized controlled studies with pre- and post-tests – a simulation study

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  • Sebastian Appelbaum - Otto-Friedrich-Universität Bamberg, Bamberg, Germany
  • Thomas Ostermann - Universität Witten/Herdecke, Witten, Germany
  • Uwe Konerding - Otto-Friedrich-Universität Bamberg, Bamberg, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF). sine loco [digital], 26.-30.09.2021. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 77

doi: 10.3205/21gmds094, urn:nbn:de:0183-21gmds0942

Published: September 24, 2021

© 2021 Appelbaum et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

Text

Very often outcome variables in health services research consist of count data. Although they are usually not symmetrically distributed, data of this kind are very often analyzed using statistical tests presupposing normal distributions. An alternative is using Poisson regression models, which are specially designed for count data. The aim of this simulation study is to compare six analysis strategies, 1) t-test, 2) Poisson regression model with MLE, 3) Poisson regression model with robust estimators 4) t-test for change scores, 5) Poisson regression with MLE with pre-test data as covariates, and 6) Poisson regression with robust estimators with pre-test data as covariates. Simulation data were generated using three different sample sizes, four average count number at pre-test, four different effect sizes and four pre post correlations. The study produced two main results: 1) the actual type 1 error probability for the MLE-Poisson regression with pre-tests as covariates does not agree with the significance level; 2) the robust Poisson regression with pre-tests as covariates seems to be the best test-strategy.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.

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