Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups
Title: Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups
Speakers: Guillermo Romero Moreno
Abstract:
Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data. We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them to our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC. Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations. Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity. In this talk, I will briefly introduce common network science approaches to multimorbidity research, present and explain our methodological approach, and present and show how our method can be easily used by the multimorbidity community via our released software package (https://github.com/Juillermo/ABC).