Assessing non-inferiority for binary matched-pairs data with missing values: a powerful and flexible GEE approach based on the risk difference
Abstract Background Clinical studies often aim to test the non-inferiority of a treatment compared to an alternative intervention with binary matched-pairs data. These studies are often planned with methods for completely observed pairs only. However, if missingness is more frequent than expected or...
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2025-02-01
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| Online Access: | https://doi.org/10.1186/s12874-025-02497-2 |
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| author | Johannes Hengelbrock Frank Konietschke Juliane Herm Heinrich Audebert Annette Aigner |
| author_facet | Johannes Hengelbrock Frank Konietschke Juliane Herm Heinrich Audebert Annette Aigner |
| author_sort | Johannes Hengelbrock |
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| description | Abstract Background Clinical studies often aim to test the non-inferiority of a treatment compared to an alternative intervention with binary matched-pairs data. These studies are often planned with methods for completely observed pairs only. However, if missingness is more frequent than expected or is anticipated in the planning phase, methods are needed that allow the inclusion of partially observed pairs to improve statistical power. Methods We propose a flexible generalized estimating equations (GEE) approach to estimate confidence intervals for the risk difference, which accommodates partially observed pairs. Using simulated data, we compare this approach to alternative methods for completely observed pairs only and to those that also include pairs with missing observations. Additionally, we reconsider the study sample size calculation by applying these methods to a study with binary matched-pairs setting. Results In moderate to large sample sizes, the proposed GEE approach performs similarly to alternative methods for completely observed pairs only. It even results in a higher power and narrower interval widths in scenarios with missing data and where missingness follows a missing (completely) at random (MCAR / MAR) mechanism. The GEE approach is also non-inferior to alternative methods, such as multiple imputation or confidence intervals explicitly developed for missing data settings. Reconsidering the sample size calculation for an observational study, our proposed approach leads to a considerably smaller sample size than the alternative methods. Conclusion Our results indicate that the proposed GEE approach is a powerful alternative to existing methods and can be used for testing non-inferiority, even if the initial sample size calculation was based on a different statistical method. Furthermore, it increases the analytical flexibility by allowing the inclusion of additional covariates, in contrast to other methods. |
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| spelling | doaj-art-9de1cc01929242d9a2a3a1eb8b71fc122025-08-20T02:01:35ZengBMCBMC Medical Research Methodology1471-22882025-02-0125111410.1186/s12874-025-02497-2Assessing non-inferiority for binary matched-pairs data with missing values: a powerful and flexible GEE approach based on the risk differenceJohannes Hengelbrock0Frank Konietschke1Juliane Herm2Heinrich Audebert3Annette Aigner4Institute of Biometry and Clinical Epidemiology, Charité – Universitätsmedizin, Freie Universität Berlin and Humboldt-Universität Zu BerlinInstitute of Biometry and Clinical Epidemiology, Charité – Universitätsmedizin, Freie Universität Berlin and Humboldt-Universität Zu BerlinDepartment of Neurology and Center for Stroke Research Berlin, Freie Universität Berlin and Humboldt-Universität Zu Berlin, Campus Benjamin Franklin, Charité – UniversitätsmedizinDepartment of Neurology and Center for Stroke Research Berlin, Freie Universität Berlin and Humboldt-Universität Zu Berlin, Campus Benjamin Franklin, Charité – UniversitätsmedizinInstitute of Biometry and Clinical Epidemiology, Charité – Universitätsmedizin, Freie Universität Berlin and Humboldt-Universität Zu BerlinAbstract Background Clinical studies often aim to test the non-inferiority of a treatment compared to an alternative intervention with binary matched-pairs data. These studies are often planned with methods for completely observed pairs only. However, if missingness is more frequent than expected or is anticipated in the planning phase, methods are needed that allow the inclusion of partially observed pairs to improve statistical power. Methods We propose a flexible generalized estimating equations (GEE) approach to estimate confidence intervals for the risk difference, which accommodates partially observed pairs. Using simulated data, we compare this approach to alternative methods for completely observed pairs only and to those that also include pairs with missing observations. Additionally, we reconsider the study sample size calculation by applying these methods to a study with binary matched-pairs setting. Results In moderate to large sample sizes, the proposed GEE approach performs similarly to alternative methods for completely observed pairs only. It even results in a higher power and narrower interval widths in scenarios with missing data and where missingness follows a missing (completely) at random (MCAR / MAR) mechanism. The GEE approach is also non-inferior to alternative methods, such as multiple imputation or confidence intervals explicitly developed for missing data settings. Reconsidering the sample size calculation for an observational study, our proposed approach leads to a considerably smaller sample size than the alternative methods. Conclusion Our results indicate that the proposed GEE approach is a powerful alternative to existing methods and can be used for testing non-inferiority, even if the initial sample size calculation was based on a different statistical method. Furthermore, it increases the analytical flexibility by allowing the inclusion of additional covariates, in contrast to other methods.https://doi.org/10.1186/s12874-025-02497-2Risk differenceConfidence intervalGEEBinary matched pairsNon-inferiority |
| spellingShingle | Johannes Hengelbrock Frank Konietschke Juliane Herm Heinrich Audebert Annette Aigner Assessing non-inferiority for binary matched-pairs data with missing values: a powerful and flexible GEE approach based on the risk difference BMC Medical Research Methodology Risk difference Confidence interval GEE Binary matched pairs Non-inferiority |
| title | Assessing non-inferiority for binary matched-pairs data with missing values: a powerful and flexible GEE approach based on the risk difference |
| title_full | Assessing non-inferiority for binary matched-pairs data with missing values: a powerful and flexible GEE approach based on the risk difference |
| title_fullStr | Assessing non-inferiority for binary matched-pairs data with missing values: a powerful and flexible GEE approach based on the risk difference |
| title_full_unstemmed | Assessing non-inferiority for binary matched-pairs data with missing values: a powerful and flexible GEE approach based on the risk difference |
| title_short | Assessing non-inferiority for binary matched-pairs data with missing values: a powerful and flexible GEE approach based on the risk difference |
| title_sort | assessing non inferiority for binary matched pairs data with missing values a powerful and flexible gee approach based on the risk difference |
| topic | Risk difference Confidence interval GEE Binary matched pairs Non-inferiority |
| url | https://doi.org/10.1186/s12874-025-02497-2 |
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