Improving the Estimation of Subgroup Effects for Clinical Trial Participants with Multimorbidity by Incorporating Drug Class-Level Information in Bayesian Hierarchical Models: A Simulation Study
| Autor: | Laura Moss, Laurie J. Hannigan, Elaine Butterly, Nicky J Welton, David Phillippo, David A. McAllister, Peter Hanlon, Neil Hawkins, Sofia Dias |
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| Rok vydání: | 2021 |
| Předmět: |
multimorbidity
Computer science Bayesian probability Context (language use) Subgroup analysis 030204 cardiovascular system & hematology Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Original Research Articles Humans Computer Simulation medical ontologies 030212 general & internal medicine subgroup analysis Estimation Hierarchical modeling business.industry Health Policy Bayes Theorem individual-patient data meta-analysis 3. Good health Clinical trial Medical ontologies Drug class Treatment Outcome Pharmaceutical Preparations Artificial intelligence hierarchical modeling business computer |
| Zdroj: | Medical Decision Making |
| ISSN: | 1552-681X |
| Popis: | Background There is limited guidance for using common drug therapies in the context of multimorbidity. In part, this is because their effectiveness for patients with specific comorbidities cannot easily be established using subgroup analyses in clinical trials. Here, we use simulations to explore the feasibility and implications of concurrently estimating effects of related drug treatments in patients with multimorbidity by partially pooling subgroup efficacy estimates across trials. Methods We performed simulations based on the characteristics of 161 real clinical trials of noninsulin glucose-lowering drugs for diabetes, estimating subgroup effects for patients with a hypothetical comorbidity across related trials in different scenarios using Bayesian hierarchical generalized linear models. We structured models according to an established ontology—the World Health Organization Anatomic Chemical Therapeutic Classifications—allowing us to nest all trials within drugs and all drugs within anatomic chemical therapeutic classes, with effects partially pooled at each level of the hierarchy. In a range of scenarios, we compared the performance of this model to random effects meta-analyses of all drugs individually. Results Hierarchical, ontology-based Bayesian models were unbiased and accurately recovered simulated comorbidity-drug interactions. Compared with single-drug meta-analyses, they offered a relative increase in precision of up to 250% in some scenarios because of information sharing across the hierarchy. Because of the relative precision of the approaches, a large proportion of small subgroup effects was detectable only using the hierarchical model. Conclusions By assuming that similar drugs may have similar subgroup effects, Bayesian hierarchical models based on structures defined by existing ontologies can be used to improve the precision of treatment efficacy estimates in patients with multimorbidity, with potential implications for clinical decision making. |
| Databáze: | OpenAIRE |
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