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Gesundheit – gemeinsam. Kooperationstagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Deutschen Gesellschaft für Sozialmedizin und Prävention (DGSMP), Deutschen Gesellschaft für Epidemiologie (DGEpi), Deutschen Gesellschaft für Medizinische Soziologie (DGMS) und der Deutschen Gesellschaft für Public Health (DGPH)

08.09. - 13.09.2024, Dresden

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Non-ignorable missing data under heterogeneity in a meta-analysis with binary outcomes

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  • Flavia Remo - Institut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena, Jena, Germany
  • Peter Schlattmann - Institut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena, Jena, Germany
  • Thomas Lehmann - Institut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena, Jena, Germany

Gesundheit – gemeinsam. Kooperationstagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Deutschen Gesellschaft für Sozialmedizin und Prävention (DGSMP), Deutschen Gesellschaft für Epidemiologie (DGEpi), Deutschen Gesellschaft für Medizinische Soziologie (DGMS) und der Deutschen Gesellschaft für Public Health (DGPH). Dresden, 08.-13.09.2024. Düsseldorf: German Medical Science GMS Publishing House; 2024. DocAbstr. 48

doi: 10.3205/24gmds095, urn:nbn:de:0183-24gmds0955

Published: September 6, 2024

© 2024 Remo 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

We estimate the pooled treatment effect size in form of the log odds Ratio and heterogeneity variance in a meta-analysis. With missingness and heterogeneity being the most common challenges that hamper data in a meta-analysis, we employ the method of finite mixture modelling combined with multiple imputation to estimate the model parameters using the EM algorithm. At the same time we impute the data that is missing not at random (MNAR) using the augmentation method simultaneously in line with the response mechanism after the work of Lehmann and Schlattmanm [1]. As we are dealing with binary outcomes, we use the standard method of considering a finite mixture of logistic regression models to estimate the regression parameters which translate into the log odds as our effect size estimate. We will illustrate our results with a meta-analysis of RCTs comparing haloperidol with placebo in treatment of schizophrenia (see [2]).

The authors declare that they have no competing interests.

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

Outline

References

1.
Lehmann T, Schlattmann P. Treatmemt of non-ignorable missing data under heterogeneity. Biometrical Journal. 2017;59(1):159-171.
2.
Higgins JP, White IR, Wood AM. Imputation methods for missing outcome data in meta-analysis of clinical trials. Clinical trials. 2008;5(3):225-239.