Journal Article

Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity

Mulder, Joris, Kavelaars, Xynthia, Kaptein, Maurits

2023

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Bayes Theorem / Bayesian multilevel multivariate logistic regression / Bayesian statistical decision theory / Brain research / Decision making / Dependent variables / Evaluation / Health aspects / Health Sciences / Hierarchical model / Humans / Logistic Models / Medicine / Medicine & Public Health / Models, Statistical / Multilevel Analysis / Multiple dependent variables / Multivariate analysis / Patients / Probability / Pólya-Gamma / Radiation therapy / Regression analysis / Statistical Theory and Methods / Statistics for Life Sciences / Stroke / Theory of Medicine/Bioethics / Treatment heterogeneity / Treatment outcome