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

Hannigan, L. J. , Phillippo, D. M., Hanlon, P. , Moss, L. , Butterly, E. W., Hawkins, N. , Dias, S., Welton, N. J. and McAllister, D. A. (2021) 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. Medical Decision Making, (doi: 10.1177/0272989X211029556) (PMID:34407672) (Early Online Publication)

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Abstract

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.

Item Type:Articles
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:McAllister, Professor David and Hawkins, Professor Neil and Butterly, Dr Elaine and Hanlon, Dr Peter and Hannigan, Dr Laurie and Moss, Miss Laura
Authors: Hannigan, L. J., Phillippo, D. M., Hanlon, P., Moss, L., Butterly, E. W., Hawkins, N., Dias, S., Welton, N. J., and McAllister, D. A.
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > General Practice and Primary Care
College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Health Economics and Health Technology Assessment
College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Public Health
College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing
Journal Name:Medical Decision Making
Publisher:SAGE Publications
ISSN:0272-989X
ISSN (Online):1552-681X
Published Online:18 August 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Medical Decision Making 2021
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
173492Combining efficacy estimates from clinical trials with the natural history obtained from large routine healthcare databases to determine net overall treatment benefitsDavid McAllisterWellcome Trust (WELLCOTR)201492/Z/16/ZInstitute of Health & Wellbeing