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

Comparison of exclusion, imputation and modelling of missing binary outcome data in frequentist network meta-analysis

Meeting Abstract

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  • Loukia Spineli - Medizinische Hochschule Hannover, Hannover, Germany
  • Chrysostomos Kalyvas - Merck Sharp & Dohme, Brussels, Belgium

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. 147

doi: 10.3205/19gmds077, urn:nbn:de:0183-19gmds0772

Veröffentlicht: 6. September 2019

© 2019 Spineli et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe



Background: Missing participant outcome data (MOD) are ubiquitous in systematic reviews as they invade from the inclusion of clinical trials with reported participant losses. There are available strategies to address aggregated MOD while considering the missing at random (MAR) assumption as a starting point. Little is known about their performance though regarding the meta-analytic parameters of a random-effects model.

Methods: We considered four different strategies to address binary MOD under MAR and applied them in network meta-analysis (NMA) [1], [2]. We classified the strategies to those modelling versus excluding/imputing MOD and to those accounting for versus ignoring uncertainty about MAR. We investigated the performance of these strategies in terms of core NMA estimates, by performing both an empirical and simulation study using random-effects NMA based on electrical network theory [3]. We used Bland-Altman plots to illustrate the agreement between the compared strategies [4], and we considered the mean bias, coverage probability and width of confidence interval to be the frequentist measures of performance.

Results: Modelling MOD under MAR agreed with exclusion and imputation under MAR in terms of estimated log odds ratios and inconsistency factor, whereas accountability or not of the uncertainty regarding MOD affected intervention hierarchy and precision around the NMA estimates: strategies that ignore uncertainty about MOD led to more precise NMA estimates, and increased between-trial variance. All strategies showed good performance for low MOD (<5%), consistent evidence and low between-trial variance, whereas performance was compromised for large informative MOD (>20%), inconsistent evidence and substantial between-trial variance, especially for strategies that ignore uncertainty due to MOD.

Conclusions: Strategies that manipulate MOD before analysis (i.e. exclusion and imputation) should be avoided as they implicate negatively the inferences. Modelling MOD, on the other hand, via a pattern-mixture model to propagate the uncertainty about MAR assumption constitutes both conceptually and statistically proper strategy to address MOD in a systematic review.

The authors declare that they have no competing interests.

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


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