gms | German Medical Science

64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

08. - 11.09.2019, Dortmund

Empirical evaluation of the implementation of the EMA guideline on missing data in confirmatory clinical trials: specification of mixed models for longitudinal data in study protocols

Meeting Abstract

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  • Sebastian Häckl - Hannover Medical School, Hannover, Germany
  • Florian Lasch - Hannover Medical School, Hannover, Germany
  • Armin Koch - Hannover Medical School, Hannover, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Dortmund, 08.-11.09.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. DocAbstr. 120

doi: 10.3205/19gmds206, urn:nbn:de:0183-19gmds2066

Published: September 6, 2019

© 2019 Häckl 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

In confirmatory clinical trials, the pre-specification of the primary analysis model is a universally accepted scientific principle to allow strict control of the type-I-error. Accordingly both the ICH E9 guideline and the EMA guideline on missing data in confirmatory clinical trials require the explicit specification of the precise model settings to such an extent that the statistical analysis can be replicated. Especially, this applies to mixed models for longitudinal data, which implicitly handle missing data. To evaluate the compliance with the EMA guideline, we evaluated the specifications in clinical study protocols from development phase II and III submitted between November 2015 and October 2018 to the Ethics Committee at Hannover Medical School under the German Medicinal Products Act, which planned to use a mixed model for longitudinal data in the confirmatory testing strategy.

Overall, 39 trials from different types of sponsors and a wide range of therapeutic areas were evaluated. While nearly all protocols specify the fixed and random effects of the analysis model (95%), only 77% give the structure of the covariance matrix used for modelling the repeated measurements. Moreover, the testing method (36%), the estimation method (28%), the computation method (3%) and the fallback strategy (18%) are given by less than half the study protocols. Almost half of the analysed study protocols (46%) neither specified all of the most influential items (fixed and random effects, covariance matrix, testing method) nor planned to do so by an additional SAP. Subgroup analyses indicate that these findings are universal and not specific to clinical trial phases or size of company. However, major pharmaceutical companies markedly specified the three main items more often compared to minor sponsors (53% vs. 14%, p<0.05), indicating a higher guideline compliance rate for major sponsors. Altogether, our results show that guideline compliance is to various degrees poor and consequently, strict type-I-error rate control at the intended level is not guaranteed.

The authors declare that they have no competing interests.

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


References

1.
Lewis JA. Statistical principles for clinical trials (ICH E9): an introductory note on an international guideline. Statistics in medicine. 1999 Aug 15;18(15):1903-42.
2.
European Medicines Agency (EMA). Guideline on Missing Data in Confirmatory Clinical Trials. 2011. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-missing-data-confirmatory-clinical-trials_en.pdf External link