Loading...
Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity
Kavelaars,Xynthia Mulder,Joris Kaptein,Maurits
Kavelaars,Xynthia
Mulder,Joris
Kaptein,Maurits
Abstract
Background: In medical, social, and behavioral research we often encounter datasets with a multilevel structure and multiple correlated dependent variables. These data are frequently collected from a study population that distinguishes several subpopulations with different (i.e., heterogeneous) effects of an intervention. Despite the frequent occurrence of such data, methods to analyze them are less common and researchers often resort to either ignoring the multilevel and/or heterogeneous structure, analyzing only a single dependent variable, or a combination of these. These analysis strategies are suboptimal: Ignoring multilevel structures inflates Type I error rates, while neglecting the multivariate or heterogeneous structure masks detailed insights. Methods: To analyze such data comprehensively, the current paper presents a novel Bayesian multilevel multivariate logistic regression model. The clustered structure of multilevel data is taken into account, such that posterior inferences can be made with accurate error rates. Further, the model shares information between different subpopulations in the estimation of average and conditional average multivariate treatment effects. To facilitate interpretation, multivariate logistic regression parameters are transformed to posterior success probabilities and differences between them. Results: A numerical evaluation compared our framework to less comprehensive alternatives and highlighted the need to model the multilevel structure: Treatment comparisons based on the multilevel model had targeted Type I error rates, while single-level alternatives resulted in inflated Type I errors. Further, the multilevel model was more powerful than a single-level model when the number of clusters was higher. A re-analysis of the Third International Stroke Trial data illustrated how incorporating a multilevel structure, assessing treatment heterogeneity, and combining dependent variables contributed to an in-depth understanding of treatment effects. Further, we demonstrated how Bayes factors can aid in the selection of a suitable model. Conclusion: The method is useful in prediction of treatment effects and decision-making within subpopulations from multiple clusters, while taking advantage of the size of the entire study sample and while properly incorporating the uncertainty in a principled probabilistic manner using the full posterior distribution.
Description
Publisher Copyright: © 2023, BioMed Central Ltd., part of Springer Nature.
Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Bayesian multilevel multivariate logistic regression, Hierarchical model, Multiple dependent variables, Pólya-Gamma, Treatment heterogeneity, SDG 3 - Good Health and Well-being
Citation
Kavelaars, X, Mulder, J & Kaptein, M 2023, 'Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity', BMC Medical Research Methodology, vol. 23, no. 1, 220. https://doi.org/10.1186/s12874-023-02034-z